# Effect of news on stocks liquidity

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4 indicators measured activity in a particular minute for a specific stock. All the indicators with their baseline means across the year are shown in Table 2 and the matching standard deviations in Table 3. Ticker Spread Executed Trades Ratio Not Executed Trades Market Sides Imbalance Trade Count Quote Count Volume AAL.L ARM.L AZN.L BARC.L BATS.L BLT.L BNZL.L BRBY.L CCH.L DGE.L GFS.L GLEN.L GSK.L HSBA.L III.L IMT.L LLOY.L MGGT.L PRU.L RIO.L RMG.L SAB.L SDR.L SHP.L SPD.L STAN.L TPK.L ULVR.L VOD.L WPP.L Table 2: Baseline values calculated across the year 4

5 Ticker Spread Executed trades ratio Not executed trades Market sides imbalance Trade count Quote count Volume AAL.L ARM.L AZN.L BARC.L BATS.L BLT.L BNZL.L BRBY.L CCH.L DGE.L GFS.L GLEN.L GSK.L HSBA.L III.L IMT.L LLOY.L MGGT.L PRU.L RIO.L RMG.L SAB.L SDR.L SHP.L SPD.L STAN.L TPK.L ULVR.L VOD.L WPP.L Table 3: Baseline standard deviations calculated across the year Statistics As the first step in the analysis, we averaged each liquidity indicator grouped by sentiment type and volume group by calculating the mean S k,t = 1 n k,t n k,t S k,t,j, where k is the stock counter within volume group, t is the time from the news event, n k,t is the number of news events for the indicator, sentiment type and volume group. Next we normalized the means j=1 T k,t = S k,t µ k σ( S k,t ) where µ k is the mean of the indicator across the year and the standard deviation of the mean calculated as σ( S k,t ) = σ(s k,t) nk,t. 5

6 Next we averaged the normalized means over time. T k = t= 5 Due to the fact that each T k,t has standard deviation 1 and there is an average correlation ρ k between the time lags, we could calculate the standard deviation of T k. σ( T 1 k ) = ρ k This gave us an opportunity to normalize again. T k,t T k = T k σ( T k ) = Tk ρ k Under null hypothesis Tk N(0, 1) and we could execute t-test in order to check if there is any effect of news on the liquidity indicator. 3 Results T-test results We ran the t-test for each of the liquidity indicators in each of the groups. The results were mixed. In the low volume group, for most of the indicators, null hypotheses were not rejected. There was one exception, namely the executed trades ratio. In the high volume group, for most indicators, null hypotheses were rejected. This means that there was significant change in the indicator when news were released. Specifically, the affected indicators were executed trades ratio, quote count, trade count and volume. Table 4 shows the detailed results of t-tests and Table 5 gives a general overview. 6

8 Bottom 10 Top 20 Indicator Negative Neutral Positive Negative Neutral Positive Executed trades ratio Quote count Trade count Volume Not executed trades - Spread - + Market sides imbalance Table 5: Results of t-tests with significance level 0.05 where + indicates positive effect and - negative effect. Comparison After analyzing which indicators had statistically significant effect from news, we compiled Table 6. It shows the indicator mean values over the whole year compared to mean values close to news. We wanted to see how large is the effect in absolute and percentual terms. It is interesting to see that most indicators,with the exception of not executed trades, have large percentual change close to news. Yet we observe that null hypotheses were not rejected in many cases. That is due to large variability in the baseline measures. 8

10 Time window In the current implementation, the time window which defines time to be close to news was defined as 5 minutes before the news item to 30 minutes after the news item. Using this definition of the time window, we calculated the results of our current research. The choice of that specific time window was arbitrary and it would be very interesting to run the program used in this researach on different time windows and compare the results. Expansion of scope Naturally, our list of seven indicators is somewhat limited. Other indicators could be easily added to current ones. Furthermore, it would be interesting to research if other markets and/or instruments show similar properties as found for stocks in the London Stock Exchange. References [1] Pandas. [Online; accessed 05-July-2015]. [2] Tonny Lybek Abdourahmane Sarr. Measuring liquidity in financial markets. International monetary fund, [3] Python Software Foundation. Python. [Online; accessed 05-July-2015]. [4] Wikipedia. Market liquidity. [Online; accessed 23-May-2015]. 10

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