Reading all the news at the same time: Predicting mid-term stock price developments based on news momentum

Transcription

1 th Hawaii International Conference on System Sciences Reading all the news at the same time: Predicting mid-term stock price developments based on news momentum Michael Hagenau University of Freiburg Matthias Hauser Karlsruhe Institute of Technology Michael Liebmann University of Freiburg Dirk Neumann University of Freiburg Abstract This paper investigates whether news momentum can predict medium-term stock index developments. News momentum can be built by aggregating tone of news over the past weeks. We find that news momentum can predict future stock price developments and establish profitable trading strategies that beat buy-and-hold and momentum benchmarks. Trades are issued for significant changes in momentum between current and prior weeks. We ensure stability of our results by using two different news data sets and by analyzing both different investment horizons and aggregation times for our news momentum. Compared to intraday news trading, medium-term momentum trading allows higher investment volumes and can contribute to complex investment decisions also incorporating other qualitative and quantitative factors. 1. Introduction The amount of available information to financial investors has substantially increased over the last decades due to improved information intermediation. The available information consists of qualitative and quantitative information of different kinds and from various sources, e.g. corporate disclosures, independent opinions, news articles and analyst reports. Since the sheer amount of information is vast and the information is mostly unstructured, there is an increasing demand for development of automated methods to understand and draw inferences from this information. As news carries information about the company s fundamentals and qualitative information influencing expectations of market participants, it plays an important role in the valuation process of investors and analysts. Thus, in recent years, many studies have focused on stock price prediction based on financial news (e.g. [12], [17], [14]). However, these predictions are only short-term oriented: They predict the immediate stock price reaction following the transmission of the news and measure the stock prices between minutes and the end of the trading day. Additionally, these studies only focus on single news messages, but do not include the momentum triggered by combinations of news into their prediction. In this paper, we focus on investment decisions that are built on news momentum to predict medium-term stock price index developments. We calculate the news momentum by aggregating tone values of single news over the past weeks. Based on news momentum of up to 12 weeks of the past, we predict future stock price index developments over one to ten weeks horizons. Based on our news momentum, we formulate two rules deciding whether a long or short strategy should be pursued during the investment horizon. We demonstrate that profitable stock index trading can be established based on news momentum for two different news sources. We calculate several performance metrics and pay special attention to the stability of our results. To demonstrate the superiority of our trading strategy, we also provide a momentum and buy-andhold benchmark to reflect typical alternative strategies. In contrast to intraday short-term predictions, medium-term predictions have two advantages: First, medium-term investments allows for significantly higher investment volumes than low-latency trades [5] as trades do not have to happen within milliseconds, but can be spread out over the day or even be placed in the very liquid open and close auctions of the trading day. Second, the news momentum may not be the sole criteria for the investment decision, but be part of a complex decision process factoring in qualitative and quantitative information as well as expert judgments. We forecast a stock price index instead of single stocks as the higher number of news contributing to the momentum ensures more stable results. Additionally, indices offer a wide variety of financial instruments (i.e. index futures) which are popular among algorithmic traders. Our findings are also supported by financial literature. Despite contradicting the efficient market hypothesis ( semi strong form, [4]) in the sense that news is not fully reflected in stock prices immediately after news is transmitted, more recent financial literature supports the view that news may need more /12 $ IEEE DOI /HICSS

2 time to be fully reflected in stock prices ([1], [7], [9], [18]). Moreover, news momentum addresses explanations of financial literature for the prolonged incorporating of news into stock prices. As objective automated measure, news momentum is not affected by subjective behavioral aspects (c.f. [3]) and maintains oversight over all relevant news for the asset (c.f. [9]). Further, we presume that there is an additional value by processing all news simultaneously. The strong relation to comprehensive stock indices indicates that news momentum may capture general sentiment changes in a country. The remainder of the paper is structured as follows: In section 2, we conduct a review of relevant research on momentum in capital markets and the prediction of stock price effects based on qualitative information. Section 3 describes how we build our news momentum based on existing research about sentiment metrics and develop our trading strategy. In section 4, we evaluate the performance of our trading strategy and compare our approach to popular benchmarks. Section 5 summarizes and describes the extension of our current news momentum model. 2. Related Work 2.1 Momentum in capital market research Since the late 80s, financial literature supports the view that price responses to new information might be delayed [1]. Consequently, the field of behavioral finance emerged and an increasing number of scholars found behavioral anomalies, such as stock price drifts and excess returns in medium to long-term trading after news (e.g. [7], [18]). The main assumption of this research stream is that there are positive future returns if there were positive returns in the past (and vice versa): There is momentum. They attributed their findings to the fact that the market responds gradually to new information and prolonged adjustment of analyst forecasts [18]. Other behavioral explanations attribute the slower incorporation of news into stock prices by overconfident investors [3] who value their own information higher than external signals. Recent literature also suggests that high numbers of concurrent news limit investor s attention and lead to delayed stock price reactions [9]. This delayed information processing exhibits potential of news-based medium-term trading. Automated analysis of news momentum also addresses two behavioral aspects from financial literature: Automation may mitigate investor s inattention and the objectivity of an automated algorithm may compensate for investor s overconfidence. 2.2 Stock price prediction based on text mining Numerous studies in financial text mining research focused on the explanation of future returns and prediction of short-term stock price reactions based on financial news. Thereby, two approaches for stock price prediction were used: One set of scholars rely on self-developed text metrics quantifying the tone of each news message (e.g. [17], [11], [10]). The classification of news messages into positive and negative is based on the text metric. The different methods vary in complexity: While Tetlock et al. only base their decision on the fraction of negative words from a psychosocial dictionary, Liebmann et al., for example, design dynamic word lists and assign weights for each word based on statistical analyses. The other set of scholars directly use machine learning approaches for the prediction of stock price reactions following a financial news message (e.g. [12], [14], [8], [15]). Most of the studies make use of a Support Vector Machine (SVM) to classify news messages into positive and negative. However, independent of the approach, previous literature is mainly focused on short-term stock price prediction, i.e. only intraday returns immediately succeeding the transmission of single news messages are considered. Fung et al. [6] investigate price reactions of up to one week which is still short compared to the investment horizons considered in financial literature (i.e. several weeks up to 12 months, [7]). The corresponding algorithms may be implemented in real-time trading systems, but are not suitable for medium or long term investment decisions. To the best of our knowledge, so far, only one working paper focuses on medium to long-term stock price prediction based on an aggregation of financial news [16]. The study uses company related news from the Reuters NewsScope data set where each news message is provided with Reuters proprietary tone score. As the methodology for this proprietary tone score is not published, it can neither be reviewed nor reproduced. Based on these tone scores, Sinha calculates a qualitative information measure over the past three months by unweighted averaging of the tone scores. He finds that the stock market under-reacts to information contained in news articles. When using for investment decisions for an up to five months horizon, his measure has predictive abilities and generates an excess return of 34 basis points per month. In contrast to [16], we make use of a publicly available and reproducible metric to base our aggregation and investment decision upon. While Sinha is only interested in contributions to the field of finance, we transform our findings into a decision support system with practical focus. Whereas Sinha

3 simulates the trading of a subsample of stocks (i.e. top and bottom deciles of tone), we simulate the prediction of a comprehensive stock price index. As a large number of stocks are represented in an index, forecasts are based on more news than for single stocks reducing noise and leading to higher more stable results. Major indices also offer futures which are very popular among algorithmic traders. Moreover, comprehensive stock indices may represent whole countries. Similar analyses can be performed for industries or economically dependent regions (e.g. emerging markets). Thus, forecasting indices is also forecasting pulse and development of economic regions. 3. Methodology In this section, we first select a suitable text metric for the tone of news from literature. Subsequently, we describe two approaches for calculating news momentum. The first approach aggregates the tone of single messages. The second approach aggregates the weekly proportion of positive news messages. Lastly, we formulate decision rules to be used for actual trading of our stock price index. 3.1 Selecting a suitable text metric for the news tone Analyzing unstructured information in the shape of text requires a machine readable representation. This step includes the identification of those words, which are most relevant and the counting of the occurrences within each news message. Although combinations of words are representing the content of text messages better than single words ([8],[14]), we make use of single words due to better availability of well-researched text metrics for the tone of messages. In contrast to the mentioned machine learning approaches, we need to rely on tone metrics to be able to aggregate the tone value of each message into a longer term news momentum. The set of available text metrics ranges from dictionary to statistical approaches which develop their word lists and weights based on actual feedback from the stock market. While dictionary-based approaches (as e.g. in [17], [11]) are striking for their simplicity, they suffer from the fix word set and a missing differentiation between different words in the dictionary (i.e. the dictionary does not come with a weighting scheme, thus, just the weights +1 and -1 are assigned for positive and negative words). Consequently, statistical approaches showed higher explanatory power and predictability for stock price returns [10]. Thus, in the following, we will make use of the statistical tone measure Tonality of [10]. For this approach, all words in the document corpus have to be extracted first. Assuming that words with the same word stem convey the same or similar meaning, we employ the Porter Stemmer, as in [13], which reduces inflected words to their stem. We also remove stopwords (e.g. the, it, like, or ) as they are of low informative value. Subsequently, the most informative words for sentiment analysis out of the remaining words have to be selected. Most informative refers in particular to those words which help to discriminate between positive and negative news messages. If, for example, a word frequently occurs in both, positive and negative news messages, it is considered to contain less information. However, if a word occurs less frequently in the overall data set, but concentrates in either positive or negative news messages, it is considered to be informative. To determine whether a news message is positive or negative in an objective manner, abnormal stock price returns on the day the news was disclosed can be used to label the messages. Thus, actual market returns are used to identify the most informative words. The market feedback reflects the average interpretation of all acting market participants which significantly differ from using a predetermined dictionary as all words are selected that play a discriminating role in the given text corpus and thus do not miss out on relevant terms that the creator of a predetermined dictionary has not thought of. The concept is similar to the information gain [10]. The just mentioned step of selecting the most informative words and assigning tonality values is very similar to the training step of a machine learning approach. Consequently, a separation of data into training and validation set is needed. After each informative word has received a tonality value, the sum of tonality values over each word in the news message is calculated and standardized by the total number of words. Thus, it ranges between -1 and +1 while negative tone values indicate negative messages and positive tone values indicate positive messages. 3.2 Trading based on news momentum In this section, we present two approaches for calculating news momentum: The first approach is based on the aggregation of tonality values. Thus, it can directly measure changes in the average tone of news. The second approach is based on aggregating the proportion of positive news (i.e. not the actual tone values). Thus, it is independent from the level of tone values and, thus, could be more resistant to

4 exaggerations in single news messages. For each approach, we describe how to build an aggregate (a) and how calculate the news momentum from changes in the aggregate to make trading decisions (b). 1. News tone momentum: a. Aggregation of tonality values: As companies with higher market value have a stronger influence on the considered stock price index, we first weight tonalities of single news messages by the logarithm 1 of the market value of the underlying companies. The weighted tonalities of the weekly news messages are averaged to determine weekly tonality values. We introduce this interim step to limit the influence of weeks with very high numbers of news [16]. For building a longer-term news aggregate, we summarize these weekly tonality values over four to twelve weeks. Consequently, e.g., the sum of 6 weeks will be a 6- week aggregate. b. Trading on news tone momentum: Every week, the current week s aggregate is compared to the aggregate four weeks prior. The portfolio is bought (i.e. long position) if the current aggregate is greater than the past value and short-sold otherwise (i.e. short position). Thus, we trade on differences of tone aggregates which we denote as news tone momentum. To reduce noise and avoid trading on minimal differences, we do not trade for very small news momenta. We chose differences of four weeks as suggested by financial literature [18]. Pilot studies also confirmed four weeks to be a reasonable time span. Significantly shorter time spans (e.g. one week) could not capture news momentum well as the two compared aggregates would have been too similar (e.g. too few news in the one week making the difference). 2. News proportion momentum: a. Aggregation of proportion of positive news: We first classify news messages into positive and negative based on their tonality value (c.f. [10]). Second, we calculate the weekly proportion factors of positive news by relating the sum of the market-value weighted frequencies of positive news to the market-value weighted frequencies of all news. With these proportion values, proportion aggregates are built in the exact same manner as for the tone aggregates, i.e. summed up for 4 to 12 weeks to build a proportion aggregate. b. Trading on news proportion momentum: Every week the value of the proportion aggregate of the current week is compared with the proportion aggregate four weeks prior. A higher current 1 We use the logarithm as there are extreme differences in market values between small and large companies proportion aggregate value is equivalent to an increased number of weighted positive news. The portfolio is bought (i.e. long position) if the current proportion aggregate is greater than the past value and short-sold otherwise (i.e. short position). Thus, we trade on differences of proportion aggregates which we denote as news proportion momentum. To reduce noise and avoid trading on minimal differences, we do not trade for very small news momenta. Building the proportion aggregate based on positive news only is fully equivalent to building it on proportion of negative news (i.e. both proportions sum up to one) as we use relative comparisons between two aggregates. The analogy to the term momentum in financial literature is obvious: Momentum in financial literature denotes the continuation of returns (i.e. changes in stock prices). News momentum denotes continued changes in the average tone of news messages (i.e. changes in tone aggregates). It is important to note that the computation of tonality values for each news message and their aggregation runs in polynomial complexity. To demonstrate the behavior of our aggregates, we plot the stock price index CDAX in comparison to the 12-week aggregate for both of our approaches (Figure 1). Our measure seems to follow economic cycles very well. Thus, we presume that news momentum could help to understand the economic situation and development of regions and countries. CDAX Tonality aggregate CDAX Aggregates CDAX Tonality Aggregate (moving avg.) Proportion Aggregate (moving avg.) Figure 1. CDAX index vs. aggregates In the following section, we will analyze the relations between CDAX and our news momentum approaches in detail. 4. Evaluation 4.1 Data set and evaluation setup Our data set comprises financial news from two different data sources: DGAP ( Deutsche Gesellschaft

5 für Adhoc-Publizität ) between 2000 and 2011 and Reuters between 2003 and Regulatory requirements in many countries (e.g. US, UK, and Germany) oblige listed companies to publish any material facts that are expected to affect the stock price by an authorized intermediate publisher, such as the DGAP. Thus, our first news set forms a preselection of relevant news from the set of available financial news. These typically include facts on deviations of financial results from earlier expectations, management changes, M&A transactions, dividends, major project wins or losses, litigation outcomes, and other types. With data coverage from 2000 to 2011, the DGAP data set features two big economic crises (i.e. dot-com and financial crisis) and is thus a suitable robustness check for trading strategies. The second data set from Reuters differs significantly from the first news set. Whereas DGAP only features corporate disclosures emitted by companies, the Reuters set comprises company related news written by Reuters correspondents. While the DGAP news set is focused on material facts required to be published by law, the Reuters set also contains other news as well. Actually, despite focusing on the same companies, only ~33% of the news actually overlaps. From both data sets, we removed penny stocks and required each message to have a minimum of 50 words in total. We impose these filters to limit the influence of outliers and avoid messages that only contain tables. Finally, we obtained 10,490 corporate disclosures from our first source DGAP eligible for our experiment. Thereof, we randomly (i.e. without temporal distinction) selected 10% of the messages for training of our tonality metric and the remaining 90% for building the news momentum. We deliberately chose the low number of training messages to increase the news base for the news momentum. As many news messages per week are essential for calculating the news momentum, we chose the training set to be as small as possible. Pilot tests showed that the smaller training set did not significantly decrease predictability for short-term subsequent stock price reactions on validation set (i.e. the remaining 90% of the data set dedicated for the news momentum). From our second source Reuters, we obtained 4,766 news articles which were used to build a separate news momentum for evaluating to what extent our approach can be generalized and to confirm our results. For quantification of prediction accuracy and achievable returns, we use a comprehensive stock price index, i.e. the CDAX index which constitutes all 584 relevant 2 stock-listed companies in Germany. We 2 All companies listed in the Prime and General Standard (as of March 2012) chose the CDAX as it contains the same companies as both of our data sets. CDAX index values were obtained from DataStream. Despite the extensive news data set over 12 years, there are only a limited number of data points for performance evaluation due to the long prediction horizon (i.e. 624 weeks and 104 six-week horizons). Thus, it is very important not to optimize for a local performance maximum, but to ensure more stable results which can be reproduced in other data sets and, most important, in practice. Consequently, we evaluate our approaches on two different data sets analyze stability exhaustively for different aggregation time spans and prediction horizons evaluate performance for two different approaches to calculate news momentum employ three different performance measures, i.e. prediction accuracy, implied return when trading according to the evaluated strategy, and OLS regressions using an extensive set of control variables analyze stability over years We also provide standard benchmarks reflecting conditions of the stock market. As there is a long-term tendency for stock-prices to increase, benchmarks are important to ensure that a proposed approach actually generated value. If, for example, the stock market went up by 4% p.a. with going up in 54.5% of the weeks, an approach delivering 5% p.a. with 55% prediction accuracy generated only marginal value. Our first benchmark reflects a buy-and-hold strategy where the portfolio is bought at the beginning of the evaluation horizon and sold at the end. This is similar to investing in the index and a typical way how investment funds benchmark themselves. Our second benchmark follows a simple momentum strategy based on findings in literature (e.g. [18]). The portfolio is bought when the index developed positively over the past weeks and shortsold when development was negative. The buy-and-hold benchmark for the Reuters data sets is a good challenge as our data set happened to set a favorable entry point. The beginning of 2003 marked the six-year-low of the index after the dot-com-crises and the tensions around the upcoming Iraq-war and, thus, was an almost perfect entry point for a buy-andhold strategy. 4.2 Results We measure performance by prediction accuracy and implied return. As the stock price index return of CDAX over the investment horizon may be either

6 positive or negative, prediction accuracy is the percentage of positive or negative returns we predicted correctly, i.e. the percentage of correct investment decision out of all investment decision. While prediction accuracy is a binary measure, the implied return measures the actual accumulated annual return that would have been achieved when trading according to our trading rules based on the recent news momentum. Table 1 presents prediction accuracy and implied return for our two news momentum approaches based on DGAP news. We list accuracy and implied return for different aggregation times and investment horizons. Aggregation time denotes the number of weeks in the past which were used for building the news momentum. Investment horizon denotes the number of weeks in the future which are used to calculate accuracy and return. The last two columns show our two benchmarks: Buy & Hold represents the average annual return if someone would have invested into a CDAX portfolio over the time of our data coverage (e.g to 2011 for DGAP). Momentum represents a stock price momentum strategy with a 6 weeks investment horizon. To give a comprehensive overview, we present results for various investment horizons and aggregation times to select the optimal values for these parameters and analyze stability. Best achieved accuracy values are up to 60%, highest returns are around 20% p.a. We observe highest and most stable accuracies and returns for investment horizons of 4-6 weeks. These horizons are in line with literature, but at the lower end [18]. Using shorter horizons also results in good, but less consistent performance (i.e. news proportion momentum performs not as well for shorter investment horizons; accuracies of news tone momentum are also reduced to below 54%). This can be attributed to the fact that effects from the news momentum are not fully Table 1. Prediction accuracy and implied return for evaluations based on DGAP data set Accuracy of news tone momentum Benchmark I Benchmark II 4 weeks 48.5% 49.5% 50.7% 54.3% 55.8% 55.6% 54.6% 6 weeks 50.2% 51.4% 55.7% 58.8% 58.6% 59.5% 54.4% 8 weeks 55.0% 54.5% 59.0% 60.4% 60.2% 57.4% 54.6% 56.7% 10 weeks 54.6% 55.2% 56.5% 57.8% 58.3% 54.7% 58.3% 12 weeks 48.8% 52.2% 55.5% 55.2% 56.8% 55.2% 58.8% Return of news tone momentum Benchmark I Benchmark II 4 weeks -5.1% -3.8% 0.8% 8.0% 9.8% 7.4% 1.4% 6 weeks 1.0% 4.5% 7.9% 12.0% 13.6% 9.6% 0.9% 8 weeks 20.4% 17.9% 18.7% 15.4% 13.0% 9.2% -0.4% 6.2% 10 weeks 25.1% 21.6% 15.6% 14.6% 9.4% 6.9% 7.6% 12 weeks 6.7% 8.8% 8.2% 8.4% 6.8% 4.4% 10.3% Accuracy of news proportion momentum Benchmark I Benchmark II 4 weeks 48.7% 50.8% 51.6% 53.9% 53.8% 53.1% 54.6% 6 weeks 49.5% 50.4% 55.8% 58.4% 57.6% 56.4% 54.4% 8 weeks 52.5% 53.3% 56.1% 58.6% 58.1% 56.7% 54.6% 56.7% 10 weeks 53.6% 52.9% 55.3% 57.5% 57.1% 54.6% 58.3% 12 weeks 49.8% 53.2% 53.4% 54.9% 56.8% 54.4% 58.8% Return of news proportion momentum Benchmark I Benchmark II 4 weeks -4.9% 1.5% 3.8% 8.5% 7.6% 6.8% 1.4% 6 weeks -3.3% 2.2% 7.8% 13.6% 8.7% 5.7% 0.9% 8 weeks 8.6% 10.2% 13.5% 9.2% 8.5% 6.4% -0.4% 6.2% 10 weeks 16.3% 13.6% 13.4% 11.6% 9.3% 5.6% 7.6% 12 weeks 4.4% 10.8% 8.0% 7.3% 9.1% 1.8% 10.3%

7 pronounced yet. Longer investment horizons than 6 weeks are also possible, but also result in less stable accuracies and returns. This might be driven by other market influences. The longer the investment horizon the more likely are major market events which are not covered by our set of company news (like e.g. Fukushima nuclear disaster, Lehman bankruptcy). For a 4-6 weeks investment horizon, best and most stable results are achieved based on the news momentum of the past 8-10 weeks. Shorter aggregations were less stable due to the lower amount of included news. Longer aggregations also reduced performance as they consider news that is too old. We highlight this investment horizon and aggregation time in Table 1 and 2 and will further denote it as a focus window. When analyzing the optimal focus window, we considered both performance metrics for both momentum approaches to ensure robustness of our results. Comparing the different momentum approaches, the news tone momentum reaches a better performance than the news proportion momentum for the DGAP data set. We attribute this to the fact that news proportion momentum only captures the proportion of positive news, but not actual tonality values. Thus, news tone momentum might capture finer changes in news momentum. Still, robustness of results is confirmed by both momentum approaches reaching their optima for the same horizons (i.e. 4-6 weeks) and aggregation times (i.e weeks). Also both momentum approaches perform significantly better than their benchmarks. Analogously to Table 1, Table 2 presents prediction accuracy and implied return based on Reuters news. Best performance for the Reuters data set (i.e. 20.7% implied return p.a.) is achieved for 1 week prediction horizon and 10 weeks aggregation time for the news proportion momentum. However, this optimum is Table 2. Prediction accuracy and implied return for evaluations based on Reuters data set Accuracy of news tone momentum Benchmark I Benchmark II 4 weeks 47.0% 48.2% 48.7% 50.1% 50.1% 51.0% 58.3% 6 weeks 51.5% 50.3% 52.5% 51.1% 53.0% 51.1% 59.5% 8 weeks 51.4% 52.7% 58.8% 56.0% 55.9% 53.8% 56.8% 62.0% 10 weeks 53.3% 53.9% 57.6% 52.8% 53.9% 50.2% 64.4% 12 weeks 51.2% 52.3% 52.9% 53.3% 51.4% 49.8% 61.6% Return of news tone momentum Benchmark I Benchmark II 4 weeks -8.4% -6.7% -5.5% 0.4% 1.2% 2.7% 1.9% 6 weeks 2.3% -1.5% 2.5% 5.4% 5.2% 5.8% 2.7% 8 weeks 1.3% 10.9% 12.3% 12.3% 7.3% 9.1% 8.3% 8.1% 10 weeks 12.5% 14.4% 11.3% 8.6% 5.2% 5.2% 11.7% 12 weeks 6.3% 9.5% 10.5% 5.1% 3.9% 3.9% 10.5% Accuracy of news proportion momentum Benchmark I Benchmark II 4 weeks 48.6% 48.3% 45.8% 47.5% 49.4% 49.9% 58.3% 6 weeks 48.8% 47.5% 47.8% 51.4% 53.9% 53.0% 59.5% 8 weeks 51.5% 50.8% 55.5% 57.3% 58.1% 58.0% 56.8% 62.0% 10 weeks 57.3% 55.0% 57.6% 56.9% 58.9% 57.3% 64.4% 12 weeks 56.2% 54.1% 54.1% 54.9% 55.9% 52.5% 61.6% Return of news proportion momentum Benchmark I Benchmark II 4weeks -7.5% -9.9% -9.6% -1.7% 0.0% 0.1% 1.9% 6 weeks -8.7% -10.4% -3.5% 4.1% 6.8% 6.7% 2.7% 8 weeks -0.4% 5.2% 7.6% 14.2% 14.1% 12.7% 8.3% 8.1% 10 weeks 20.7% 17.0% 15.1% 15.4% 15.2% 12.2% 11.7% 12 weeks 16.8% 16.4% 11.1% 9.5% 9.1% 8.0% 10.5%

8 neither confirmed by the accuracy metric nor the news tone momentum. Thus, we still focus on the 4-6 weeks investment horizon at 8-10 weeks aggregation time. An important note relates to the stability of results for different investment horizons. For longer investment horizons, we witness lower stability of results as they are based on less data points. For example, for the 10-week horizon, we base our results on only one tenth of the data points as only every tenth week is invested. We also look at stability over years to ensure that results can be generalized and are not a local optimum (Table 3). To provide convincing results that can be applied in practice, it is important that performance is delivered in almost every year. We observe higher stability (i.e. lower volatility) for news momentum based training. Returns of benchmarks are relatively high in good years, but less stable considering all covered years. While benchmark I had five years of negative return and benchmark II four years of negative, our DGAP-based news momentum trading strategy had no year of negative return. Further, our news momentum trading strategy did not have any year with very negative returns (e.g. less than -10%). Reuters-based trading again performed worse than DGAP-based trading, but performed more stable than the benchmarks. Our news momentum predicts falling markets better than rising markets. Thus, in years of falling stock price (e.g. 2002, 2008), we achieve higher accuracies and returns. This behavior is in line with the financial literature finding that negative messages have higher drifts after news [2]. In our final evaluation, we analyze how well news momentum predicts actual return values of our index (instead of the binary measure accuracy ). By using multivariate regressions, we can also examine the influence of other variables on index returns. We still use the investment horizon of 6 weeks which was found best-performing when considering accuracies and implied returns. We use a set of eight ordinary least square (OLS) regressions to investigate how the aggregated news momentum over the past nine weeks predicts actual CDAX returns over a six week investment horizon (see Table 4). The news momentum is calculated for both of our approaches: For each approach, it is the delta of the actual 9-week aggregate and the 9-week aggregate four weeks prior. To analyze autocorrelations, we use the past three 4-week (i.e. monthly) CDAX returns as control variables as suggested by [18]. We also control for the market value of equity (ln(mv)). As we do not predict single stock returns, but a comprehensive stock price index, we aggregate the market values of index participants according to the CDAX weighting. We also include years as dummy variables to account for cyclical effects. Furthermore, we tested for heteroscedasticity and multicollinearity to ensure that no abnormality confounds the experiments results. The results are presented in Table 4: It shows regression coefficients, t-statistics in parentheses and adjusted R² for each of the regressions. Significance level is indicated by stars: *** indicate = 0.1%, ** indicate = 1%, and * indicates = 5%. Estimations (1), (2), and (3) examine the association between CDAX returns of 6-week investment horizons and our news tone momentum for our different data sources. Estimations (4), (5), and (6) examine the same association for news proportion momentum. Both news tone momentum and news proportion momentum is significant at = 0.1%-level (indicated by ***) at t-statistics of 4.13 and Positive coefficients indicate that a higher news momentum Table 3. Stability over years for news tone momentum (8 weeks aggregation time, 4 weeks prediction) Accuracy Accuracy Accuracy Accuracy Return Return Return Return DGAP Reuters Benchmark I Benchmark II DGAP Reuters Benchmark I Benchmark II % 46.2% 55.8% 29.9% -8.1% 13.0% % 49.0% 49.3% 8.4% -17.9% 1.1% % 41.5% 54.9% 28.9% -39.9% 22.8% % 63.4% 54.7% 77.3% 27.5% 26.6% 40.1% 26.6% % 68.4% 55.8% 51.9% 10.2% 17.1% 7.4% -3.5% % 60.1% 67.3% 70.6% 0.2% 6.9% 28.2% 17.3% % 61.9% 66.0% 75.5% 6.7% 3.2% 23.1% 16.9% % 42.6% 67.3% 53.9% 0.4% -6.0% 20.4% 4.1% % 51.9% 35.8% 46.7% 44.6% 17.0% -40.5% 2.6% % 57.7% 56.0% 34.0% 23.0% -9.3% % 61.5% 40.7% 14.4% 18.1% -7.3% % 52.1% 46.1% 35.0% -14.1% -6.6%

9 Table 4. Regression results for prediction of 6-week CDAX return (1) (2) (3) (4) (5) (6) (7) (8) Investment Horizon 6 weeks 6 weeks 6 weeks 6 weeks 6 weeks 6 weeks 6 weeks 6 weeks News tone momentum (DGAP) News tone momentum (Reuters) News proportion momentum (DGAP) News proportion momentum (Reuters) CDAX Return 4 to 1 weeks ago CDAX Return 8 to 5 weeks ago CDAX Return 12 to 9 weeks ago ln(mv) *** (4.13) (-1.90) (-1.29) (-0.07) (-0.74) *** (3.13) (0.05) (0.12) (-0.17) (-0.28) ** (2.88) ** (3.12) (-0.59) (0.45) (-0.58) (0.05) *** (3.57) (-1.73) (-1.22) (-0.34) (-0.91) *** (3.50) (-0.15) (-0.45) (0.09) (-0.02) (1.96) * (2.64) (-0.51) (-0.08) (-0.02) (0.13) (1.92) (0.11) (-1.87) (-1.28) (-0.08) (-0.70) (1.56) * (2.12) (-0.07) (-0.37) (-0.24) (0.09) N Adjusted R² 22.2% 22.2% 32.8% 18.0% 25.4% 29.8% 20.5% 27.7% predicts higher CDAX returns. Relations for news momentum based on Reuters news remain slightly weaker for both approaches, but are still significant (i.e. t-statistics of 3.13 and 3.50). The number of observations ( N ) also varies with the data set as the Reuters data set covers fewer years. The adjusted R² for regressions based on the DGAP data set reaches ~18-22%. This good result confirms the strong visual correlation between CDAX and our aggregates in Figure 1. Regressions based on the Reuters data set reach an even higher adjusted R² of ~22-25%. However, as they are based on fewer data points, we cannot infer that explanatory power is higher. The inclusion of both data sets in one regression demonstrates that it is beneficial to base decision on information from both data sets (i.e. adjusted R² increases from ~18-25% to ~29-32%). The comparison of tone momentum and proportion momentum in estimations (7) and (8) reveals slightly more explanatory power for tone-based momentum on the DGAP data set (vice versa for the Reuters data set). The logarithmic market value and past CDAX returns do not reach significance. 5. Conclusion In summary, our research shows that a profitable trading strategy can be established based on the news momentum of the past weeks. Prediction accuracies of up to ~60% are achieved, implied returns of 20% p.a. are reached and exceeded. Stability of results was checked for different years on two different data sets. Thus, news cannot only be used for short-term trading, but also for medium-term investments. Evaluation showed that news tone momentum overall performed slightly better than news proportion momentum. We attribute this to the fact that news proportion momentum only captures the proportion of positive news, but not actual tonality values. Thus, fine changes in tonality value may only be captured by news tone momentum. News momentum supports investment managers to maintain oversight and incorporate all relevant available news into their decisions. Furthermore, news momentum contributes to more objective decisions as no subjectivity in interpretation of news articles is involved. Trading on medium-term horizons has several advantages over short-term low-latency trading. First, higher investment volumes are possible. Second, it also allows for manual control and intervention. This is very relevant for those fund managers who would not trust a fully automated system. Even if not implemented in a low-latency trading system, it might be beneficial for investment managers as news momentum is an objective measurement of all available information. Lastly, as news momentum also may predict stock price indices, trading on major index futures is feasible. Many stock price indices are representative for national economies. As news momentum is a good

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