Investor Performance in ASX shares; contrasting individual investors to foreign and domestic institutions. 1 Reza Bradrania a*, Andrew Grant a, P. Joakim Westerholm a, Wei Wu a a The University of Sydney Business School, Sydney 2006, Australia Executive Summary This paper investigates the short-term relation between trading of the aggregate population of individual investors and stock returns across all stocks on the Australian Securities Exchange. We obtain data from the clearinghouse, which categorises traders into individuals, institutions, and nominees. We examine order imbalance and stock returns in trades that reach the clearing process (that is, not reversed intraday), rather than relying on proxies related to broker types or a subset of trades. Stocks with abnormal buying pressure by individual investors underperform stocks with abnormal selling pressure over the subsequent three days, with a one-day return of -93 basis points. We show that nominee accounts, representing mostly foreign investors, gain from taking the opposite side of individual trades over one to three days after the trade. Over the 5 to 20 days horizon, stocks that individuals intensively trade exhibit return reversals. This return pattern can be explained by domestic individuals taking liquidity from foreign investors. Using trades that reach the clearinghouse makes it likely we observe relatively aggressive orders, rather than the passive, liquidity-supplying limit orders favoured by intraday traders. However, we argue that individual trades reaching clearing we study are likely to be from non-professional traders with a relatively long time horizon, and therefore more representative of the general population of individuals than liquidity providing day traders. The negative association between individual buying intensity and subsequent returns are stronger and more persistent in smaller shares where individual holdings are expected to dominate, while the effects reverse more rapidly in larger capitalisation shares where institutions dominate holdings and trading. The results from this study support policy recommendations for regulation that protects less informed investors from overpaying for liquidity through sub-optimal trading strategies when they enter and exit the market, which excessively benefits the counterparties to their trades. 1 This research was supported by the Centre for International Finance and Regulation (project number T008), which is a Centre of Excellence for research and education in the financial sector, funded by the Commonwealth and NSW Governments and supported by other consortium members (see www.cifr.edu.au). 1
Summary of Results It is common in the literature to treat individual investors as a distinct category of investors and their trading behaviour and performance is typically contrasted to other distinct investor categories such as institutions and foreign investors. There are good reasons to expect these categories to have some common characteristics and to differ from the other categories due to different incentives: managing their own money vs. investment management mandates, long investment horizons vs. regular performance evaluation, access to less capital and limited information acquisition capabilities vs. very large funds under management and access to ample resources of information. In our data we observe that the three most active investor categories in the Australian market are individuals, superannuation funds and nominees (the nominee category of investors mostly represent foreign institutional interests). In our analysis we focus on the interaction between individuals and nominees, as the aim of this project is to investigate the significance of the interaction between these two investor categories, which commonly take the opposite sides in trades. It has been observed in the literature that individual investors tend to invest as a group, favouring certain stocks during certain time periods such as after news, earnings releases, analyst coverage initiation and upgrades. We measure individual buying pressure in each stock as the difference between the total value of shares bought, minus the total value of shares sold by individuals during a day. We scale this by the average value of trading by individuals in this stocks over the previous year. We then rank each stock each day based on this individual buying pressure and go on to observe the performance of stocks that have high buying pressure to stock that have low buying pressure. We take the difference between the return in stocks with high buying pressure and stocks with low buying pressure, and measure the significance of this difference day by day after the trade. Figure 2 describes the difference in return between shares with high and low individual buying pressure and Figure 3 shows the difference in return between shares with high and low nominee buying pressure. Table 1 provide the numerical statistics used to produce Figure 1, the individual investor buying pressure tests. Further details are available in the complete research report. To measure the difference between stocks that individual investors typically hold vs. stocks that are dominated by institutional investors we categorise our sample into market capitalisation categories that have relevance to what can be observed from shareholding records. In the sample of large stocks we include the largest 50 firms. In the medium stock sample we include the rest of the stocks in the ASX 200 index outside of the top 50 firms and in the small stock sample firms which do 2
not belong to the ASX 200 index. The average capitalisation for an average large stock in our sample is $16,322 million top 50. The number falls to $1,547 million for an average medium stock and $123 million for an average small stock. The average percentage spread for all sample stocks is 4.82%, but it varies significantly from 0.23% for the average large stock to 5.35% for the average small one. The average individual holding for all stocks is about 14% implying that most of the investors at the ASX are institutions as we expect. Figure 1 shows the distribution of shareholdings by major investor categories during the investigated period. The main results of this study is that he results of this investigation show that individual investors are particularly vulnerable to exploitation by nominee investors (foreign institutions) during the three days after they buy or sell. Specifically this research describes the short-horizon performance of stocks with heavy retail buying pressure over the period 2009-2014. The findings indicate that stocks fall by a cumulative 82 basis points (bps) on the subsequent three days after intense individual investor buying activity. Stocks with intense selling pressure by individuals rise by a market-adjusted 91 cumulative bps over a similar horizon. Nominee investors, in contrast, display a pattern of returns with a cumulative abnormal 46 bps decrease following intense selling activity, and 109 bps increase following intense buying. These short-term patterns in returns are effective price concessions paid by individual investors, and do not fully return to a zero return over a 20 day horizon. The results are consistent with individuals demanding liquidity and being forced to provide compensation to institutional investors for this service. This research contributes to the literature by directly investigating all individual investor trades in the Australian market, while previous studies rely on a subset of individual investors that may represent a specific type of investors with regards to how informed they are and what their purpose of trading is. It is shown that orders submitted by individual investors are vulnerable to exploitation by foreign investors (represented by nominees). Policy Recommendation based on the presented evidence Several policy recommendations are provided on the basis of the results from this study. Less informed investors, which categorises a majority of the individual investors in the population, are in this study shown to overpay for liquidity when they enter and exit the market, which benefits foreign institutions. There is hence a need for incentives or regulation that motivates and requires brokers and financial advisors to monitor the trading strategies of their clients and to provide them 3
with the education and tools needed to avoid excessively aggressive or too passive trading strategies, for example to use marketable orders only selectively and to not leave limit orders for long times in the order book. We show that this is particularly important in low capitalisation shares. This research provides methodology that can be used to detect those investor accounts that are most at risk of being exploited by better informed investors. This may be particularly important in Australia where based on the results in this study individuals ownership in shares by individuals is relatively large in international comparison, potentially due to a larger proportion of pension funds invested in equities (generally perceived to be at a median of 60% compared to 50% in the US). Investor protection efforts by ASX are exemplary and have been more successful than many international counterparts (source ASX Chairman The Age on 13 July 2010). The form of investor protection related policy recommendations in this paper are different. While ASX is focusing on educating investors about risks in using certain investment advisors and investment products, we focus on dangers in using certain execution and investment strategies. In this area there is still room for further education of investors and possibly further regulation. Brokers fiduciary duties supervised and enforced by ASIC typically concern serious breaches such as mixing of client funds and own, unauthorised trading etc. We propose that fiduciary duties of brokerage firms and financial advisors should be expanded to a duty to NOT EXPOSE LESS INFORMED INVESTORS TO PICKING OFF RISK BY BETTER RESOURCED AND SKILLED INVESTORS. This would achieve a better alignment of the interests of financial firms with their customers as called for by the Financial System Inquiry 2015. In practice this can be achieved by motivating brokerage firms and advisors to better monitor what their clients transactions, particularly in their online platforms, and to educate clients to avoid exposure to unnecessary systematic risk in using sub-optimal trading and investment strategies. The aim would be to guarantee that consumers of financial services are only exposed to intended market and unique risk of their investments, not to risk associated with their choice of execution and investment strategies. The losses incurred by individual investors in this study may also be due to behavioural biases, indicating that many Australian individual investors need to re-think their justification for direct share investments. Further research Utilising the databases and analytical tools developed by this project, the research team will continue to investigate the trading patterns of individual investors to further isolate problem areas 4
and to advice strategies to avoid these. Interesting topics will be to compare boom vs. bust periods with different levels of investor sentiment, high vs. low volatility environments and to further elaborate on findings in this paper, that the effect of suboptimal trading strategies by individual investors are stronger in low capitalisation and less liquid securities. We like to further explore what type of behavioural biases such as overconfidence and loss aversion that may motivate investors to use suboptimal strategies when they enter and exit markets. Up until now our results indicate that the behaviour of individual investors is quite rational, with the information they have available they do not concern themselves with the timing of market entry and exit in the short run, as their holding periods and investment horizons are longer than those of the liquidity providers and the institutions. 5
Dollar Value in 000,000,000s $ Figure 1 Dollar Shareholdings by Investor Category This figure presents the dollar value of shares held by investors during the period between 1 January 2009 and 19 August 2014. CHESS reports the number of shares held by one of the three investor categories, which are domestic individuals, foreign investors and domestic institutions, for each stock every day. We compute the dollar value of shareholdings by each investor category with Volume-Weighted Average Price (VWAP). We then aggregate the dollar holdings for each investor category across the sample stocks and report the daily time series of total dollar holdings over the sample period. 1400 1200 1000 800 600 400 200 0 01/2009 07/2009 01/2010 07/2010 01/2011 07/2011 01/2012 07/2012 01/2013 07/2013 01/2014 07/2014 Foreign Investors Domestic Institutions Domestic Individuals 6
Figure 2 Difference in returns between Daily Cumulative Market-adjusted Returns for Portfolios of Intense Individual Buys - Sells and for Portfolios of Portfolios of Intense Nominee Buys Sells. The figures presents the daily cumulative market-adjusted returns on the zero-cost portfolios that are long in the intense buying and short in the intense selling portfolios, formed on the basis of daily Net Imbalance Trading measure (NIT) of individuals and of foreign investors for 5 days following the formation day. Panel A presents returns for individual investor and Panel B for nominees. We sort sample stocks on each day based on their daily NIT measure, from the smallest to the largest, and construct quintile portfolios. The top portfolio (quintile 1) is the portfolio that individuals sell more than they buy (intense selling portfolio) and the bottom portfolio (quintile 5) is the portfolio that individuals buy more than they sell (intense buying). We compute the cumulative market-adjusted returns between day [t+1, t+x] where t is the day of portfolio construction and x is the number of trading days after t. For each intense trading portfolio we average returns on individual stocks in the portfolio and then adjust by subtracting the return on a market proxy (an equally-weighted market portfolio) over the same period from the portfolio returns. Panel A 0.002 Individual Investors 0-0.002 t+1 t+2 t+3 t+4 t+5-0.004-0.006-0.008-0.01-0.012 Small Stocks Medium Stocks Large Stocks Panel B Nominees 0.012 0.01 0.008 0.006 0.004 0.002 0-0.002 t+1 t+2 t+3 t+4 t+5 Small Stocks Medium Stocks Large Stocks 7
Table 1 Daily Market-adjusted Returns for Portfolios of Intense Individual Trading This table presents market-adjusted returns for equally-weighted portfolios formed on the basis of daily Net Imbalance Trading measure (NIT), with the net trading imbalances of individuals. We sort sample stocks on each day based on their daily NIT measure, from the smallest to the largest, and construct quintile portfolios. The top portfolio (quintile 1) is the portfolio that individuals sell more than they buy (intense selling portfolio) and the bottom portfolio (quintile 5) is the portfolio that individuals buy more than they sell (intense buying).this procedure results in daily time series of trading quintile portfolios. We conduct the same procedure for each size group and we present results for extreme portfolios (quintile 1 and quintile 5). We compute the daily returns for each stock in the portfolio between its VWAP of the previous trading day and the VWAP of the current trading day. The portfolio returns are calculated by averaging returns on individual stocks and then adjusted by subtracting the return on a market proxy (an equally-weighted market portfolio) over the same day from the portfolio returns. We also calculate the market-adjusted returns for the zero-cost portfolios that is long in the intense buying portfolios and short in the intense selling portfolios. We present the time series average of the daily market-adjusted portfolio returns and t-statistics (in parentheses) for the day of portfolio construction (0) and the 5 days subsequent to portfolio construction. Size Portfolio trade trade + 1 trade + 2 trade + 3 trade + 4 trade + 5 All Intense Selling 0.0040*** 0.0047*** 0.0038*** 0.0006*** 0.0000-0.0001 Stocks (quintile 1) (32.6) (21.6) (19.0) (5.01) (-0.42) (-1.12) Intense Buying -0.0032*** -0.0046*** -0.0029*** -0.0007*** -0.0002* -0.0002* (quintile 5) (-13.3) (-31.9) (-18.6) (-5.96) (-1.71) (-1.86) Buy minus Sell -0.0072*** -0.0093*** -0.0067*** -0.0012*** -0.0002-0.0001 (Q5-Q1) (-27.0) (-29.3) (-21.2) (-7.49) (-1.00) (-0.77) Small Intense Selling 0.0041*** 0.0053*** 0.0047*** 0.0008*** 0.0002 0.0001 Stocks (quintile 1) (23.2) (17.0) (15.8) (5.18) (1.41) (0.76) Intense Buying -0.0017*** -0.0047*** -0.0031*** -0.0006*** 0.0000-0.0001 (quintile 5) (-5.44) (-25.7) (-16.1) (-4.12) (-0.23) (-0.34) Buy minus Sell -0.0058*** -0.0100*** -0.0078*** -0.0015*** -0.0002-0.0002 (Q5-Q1) (-18.8) (-23.9) (-19.3) (-7.1) (-1.09) (-0.78) Medium Intense Selling 0.0036*** 0.0034*** 0.0020*** 0.0000-0.0006*** -0.0007*** Stocks (quintile 1) (19.0) (14.9) (9.00) (0.06) (-3.28) (-3.57) Intense Buying -0.0076*** -0.0046*** -0.0024*** -0.0012*** -0.0008*** -0.0007*** (quintile 5) (-30.66) (-20.74) (-12.12) (-6.12) (-4.1) (-3.21) Buy minus Sell -0.0113*** -0.0081*** -0.0044*** -0.0012*** -0.0002 0.0000 (Q5-Q1) (-40.2) (-26.8) (-18.0) (-5.98) (-0.99) -0.04 8