Future Market Returns Research Articles

Informed Trading and Return Predictability in China: Research Based on Ensemble Neural Network

ABSTRACT We construct a new informed trading index based on the high-frequency trading data of the Chinese A-share market using the ensemble neural network algorithm. We find that the informed trading index is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R 2 of 5.45% and 3.53%, respectively, which is far greater than the predictive power of other previously studied informed trading indicators and macroeconomic variables. The asset allocation strategy based on our index can generate large economic gains for the mean-variance investors, with annualized CER (certain equivalent return) gains ranging from 10.91% to 7.80% as the investor’s risk appetite varies. The driving force of the predictive power appears to stem from the liquidity provider role that informed traders play, which decreases the market’s illiquidity risk and lowers the risk premium of equity. Our analysis complements the returns predictability study by adding a new predictor on the one hand and informs market timing strategies on the other.

The Role Of The Green Economy, Technological Innovation, Stock Market Advantages In Sustainable Development

A green economy to achieve sustainable development provides technological innovation and stock market profits in a sustainable social and environmental economy. To accelerate the transition to an environmentally friendly economy, companies need to understand the impact of implementing environmentally friendly policies and activities on the economy. Results show that green economic growth policies produce future aggregate stock market returns are lower, consistent with the perceived reduction in risk argument by investors. As this research increases our understanding of how the transition to a more sustainable green economy can impact the aggregate profitability of financial markets, implementing a green economy is important to implement appropriate measures to support it sensitively without incurring large costs that adversely impact the health of the economy. Increasing the driving force of technological innovation is the need to comply with environmentally friendly transformation in the economy and realize high-quality economic growth in technological innovation that does not provide support for green economic growth, but over time the impact of technological innovation on economic growth will change to positive. This shows that increasing the ability of technological innovation to encourage environmentally friendly economic efficiency.

The Korean Spotlight: The Effect of Culture on Stock Returns Through Investor Attention*

AbstractThis study empirically examines the relationship between “investor attention” and aggregate stock returns, seeking to determine whether interest in South Korea can predict the returns of the Korean stock market. The results confirm a positive relationship between interest in Korea and future stock market returns. Furthermore, this relationship is observed to have emerged with the widespread dissemination of Korean culture starting in 2017. This impact is found to be more significant on the KOSDAQ market compared to the KOSPI market. Within the KOSPI market, the influence of interest on stock returns is particularly pronounced in the case of small‐cap stocks. It is also observed that since 2017, as interest in South Korea has increased, information related to the improvement of Korea's corruption index has begun to significantly affect stock returns.

Neural network and machine learning use cases: Indian bond market predictions

This study examines machine learning techniques to investigate how artificial intelligence (AI) affects predicting future trends in the bond market. The bond market offers a global perspective on capital costs for a business by establishing the fair value of the bond issue, which is based on multiple factors. The asset price market, which has employed machine learning (ML) and deep learning (DL) techniques to address the primary forecasting difficulty, surprisingly plays a significant role in predicting future bond market returns. As an outcome, if this gap can be forecast, it can act as the bond market's data-driven long-term direction and yield additional profits. Daily security-specific data for the 10-to-3-year Indian Treasury Bond (ITB) was gathered from 2013 to 2022 and is available in the global government bonds database. The researchers looked at how well the auto-regressive integrated moving average (ARIMA), linear regression, and deep recurrent neural network-long short-term memory (DLSTM) models could predict bond yields and returns in future bond markets. The empirical results demonstrate that the DLSTM models most fairly predict the price of government bonds over both the short and longer horizons when compared to ARIMA and linear regression.

When Bankers Go to Hail: Insights into Fed–Bank Interactions from Taxi Data

We introduce taxi ridership between the Federal Reserve (Fed) Bank of New York and large financial institutions headquartered in New York City as a novel proxy for Fed–bank face-to-face interactions. We document a negative relation between past Fed–bank interactions and future stock market returns, particularly on days around the Fed’s public announcements. We also find significantly elevated Fed–bank interactions immediately following the lifting of the Federal Open Market Committee blackout. Our findings suggest that the Fed increases its information gathering via face-to-face interactions when it possesses negative private information about the condition of the economy. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the Muma College of Business Center for Analytics and Creativity at the University South Florida, the George Stigler Center for the Study of the Economy and the State, the Lynde and Harry Bradley Foundation, the University of Chicago Booth School of Business, the Liew Fama-Miller Fellowship, and the Fischer Black Fellowship. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4885 .

An enhanced investor sentiment index*

We propose an enhancement to the well-known market-based investor sentiment index by Baker, M., and J. Wurgler. (2006. “Investor Sentiment and the Cross-Section of Stock Returns.” The Journal of Finance 61 (4): 1645–1680). The low forecasting power of that index for future aggregate stock market returns has long been a puzzle, and we demonstrate that its ability to empirically capture latent investor sentiment can be significantly improved by allowing contributions of its individual components to vary over time, rather than being fixed. Our enhanced index represents an improved measure of investor sentiment: empirically, we find that our sentiment index not only instils forecasting power to the original Baker and Wurgler (2006) index, it also outperforms competitor measures in empirically capturing unobservable sentiment. This superior empirical performance is demonstrated to be due to investors’ irrational expectations about future discount rates. Lastly, sentiment measured by our enhanced index contains useful and unique information about future market returns, as it outperforms fundamental economic predictors in forecasting, in both the statistical and economic sense.

On the relationship between sentiment gap and A-share premium in China

In this study, we find that the lagged sentiment gap between the A and B share markets in China inversely predicts the price premium of A-shares over B-shares. We also discover a positive concurrent relationship, implying that the sentiment gap drives the premium. More importantly, the premium itself not only conversely forecasts future market returns for boards comprising 64% of all stocks but also shows a positive concurrent relationship, fulfilling the role of a sentiment proxy. Finding the role enriches the importance of the premium since prior research focuses solely on its rationales.

Optimizing Stock Portfolio using Markowitz Model

The stock market is a well-known illustration of an investing option with obvious returns. Its volatility provides a basis for carefully analyzing its prior results in an effort to forecast future market returns. This paper discusses the various models used for selecting investment portfolios and utilizes the Markowitz decision-making model to aid in selecting an optimal investment portfolio based on historical price data and US Treasury 10-year bond rate data. Four stocks were included in the model, with two popular and highly volatile stocks, and two fast-growing stocks. The Markowitz model that maximizes the Sharpe ratio favors the two fast-growing stocks, whereas the same calculations for minimizing risk favor the more volatile stocks, but only lowers risk marginally and reduce returns significantly. The paper further analyzed the accuracy of the Markowitz model in this specific case and provided the Fama French model as a usable alternative that will likely provide alternative suggestions that are more correlated to real-life scenarios.

Do Exogenous Shocks in Macroeconomic Variables Respond to Changes in Stock Prices?

The research aims to examine the unexpected changes in stock prices due to external shocks given to the macroeconomic variables to forecast future stock market returns. The study applies two econometric models such as «Variance Decomposition» (VDC) and «Impulse Response Function» (IRF) for examining the exogenous shocks in macroeconomic variables respond to changes in stock prices. Monthly time series data of five significant macroeconomic variables Real Exchange Rate, Interest Rate, Consumer Price Index (CPI), Crude Oil Prices, and Trade Openness, taken as independent variables and BSE SENSEX as a dependent variable. The research period is from Jan 2009 to Dec 2019. The study has taken the responsibility to reveal a few strong evidences for changes in stock prices due to exogenous shocks in Exchange Rate, Trade Openness, Inflation, and Interest rate along with crude oil prices. According to the results, changes in the stock market are due to external factors like changes in dividend policy or capital loss, and some changes in the stock market are due to its own innovative shocks. This study suggests to reduce unexpected changes in stock prices frequently, companies should control capital loss and focus on stable return/dividend policies. There are divergent views in the literature review in the context of measures of these variables, however no research has been done on exogenous shocks in macroeconomic variables to BSE SENSEX for the Indian stock market with this particular data set and duration.

Belief-based momentum indicator and stock market return predictability

Li et al. (2022) propose a new momentum indicator that combines past returns and consistent belief information, and show that the indicator positively predicts cross-sectional stock returns. Based on the momentum indicator of Li et al. (2022), we further develop a conditional past return (CPR) indicator that additionally adds the direction information for the investors' consistent belief. We examine the effectiveness of CPR as a predictor for stock market returns. Our evidence shows that CPR significantly and positively predicts future one-month market returns. And CPR provides unique predictive information that is not related to the other popular predictors. The abundant out-of-sample evidence further supports CPR’s predictive ability. Additionally, we detect the asymmetric role of CPR in predicting market returns and find that much of the predictive ability of CPR is attributed to the interaction between the positive past returns and the positive consistent belief.

The change in stock-selection risk and stock market returns

Following Jiang et al. (2021), who propose a stock-selection opportunity (SSO) measurement by the absolute average positive alpha of individual stocks to reflect stock-selection timing, we construct a stock-selection risk (SSR) measure from the perspective of negative alphas of individual stocks. Then, we investigate the predictive abilities of SSO, SSR, the change of SSO (CSSO), and the change of SSR (CSSR) on stock market returns. By using data from 2003 to 2020 in China, we find that only CSSR significantly predicts future one-month market returns. Moreover, considering other popular predictors, our in-sample and out-of-sample results and a series of robustness checks support the proposal that CSSR provides unique information for predicting market returns that reduces forecast errors and increases economic value for investors. Furthermore, our trading activity evidence shows that CSSR predicts stock market returns due to investors' underreaction to the information of CSSR.

Average skewness in global equity markets

AbstractThis paper examines the predictive power of average skewness, defined as the average of monthly skewness values across stocks, documented by the prior literature for US market returns in an international setting. First, we confirm the validity of the results in the original study and show that the intertemporal relation between average skewness and aggregate returns becomes weaker in an alternative sample period. Second, when we repeat the analysis in 22 developed non‐US markets, we find that average skewness has no robust predictive power for future market returns. The loss of forecasting power in the international sample does not depend on the method used to calculate average skewness or the regression specification and is supported by additional out‐of‐sample tests and subsample analysis.

Forecasting US stock market returns by the aggressive stock-selection opportunity

We propose a measurement of aggressive stock-selection opportunity based on positive alphas and idiosyncratic volatilities of cross-section stocks, and examine the role of aggressive stock-selection opportunity in predicting stock market returns. For the US stock market, we find that the change of aggressive stock-selection opportunity has a significant and negative coefficient for predicting future one-month market returns. The out-of-sample results also show the change of aggressive stock-selection opportunity improves the return forecasting performance and increases investors’ economic values. In particular, the predictive information of the change of aggressive stock-selection opportunity is independent of traditional macroeconomic predictors. The economic channel evidence shows that the change of aggressive stock-selection opportunity increases future market volatility and then results in lower market returns.

Do Natural Disaster Experiences Limit Stock Market Participation?

AbstractWe examine whether natural disaster experiences affect households’ portfolio choice decisions. Using data from the National Longitudinal Survey of Youth 1979, we find that adversely affected households are less likely to participate in risky asset markets. After a disaster shock, households become more risk-averse and lower their expectations on future stock market returns. Such conservative portfolio choices persist even after households relocate to less disaster-prone areas, consistent with risk preferences being altered by disaster experiences. Overall, our evidence suggests that transient but salient experiences can be an important factor in explaining the limited participation puzzle.

An Investigation of Weak Form Efficiency in the Nigerian Capital Market

The study investigated if the Nigerian stock market (NSM) is weak and inefficient from1990 and 2019. To properly capture the studied periods, they were clustered, the data used to conduct this research is monthly All Share Index (ASI) source from the apex bank Bulletin, 2019. The specific objectives investigated include: (1) presence of normal distribution, (2) the randomness or stock market returns’ independence so as to forecast future market returns. Various tests conducted includes: descriptive statistics, Kolmogorov-Smirnov Test, runs test, unit root test, and simple regression. The results are as follows: (1) the NSM is highly risky;(2) The NSM returns is not normally distributed in the period between 1990 and 1997; 1998 and 2004; 2005 and 2015, and in the whole periods (1990 to 2019) though was normally distributed between 2011 and 2014 (3) the NSM seems to be inefficient from 1990 to 1997, 1998 to 2004, 2005 to 2015 and in the whole periods (1990 to 2019); (4) the market fashioned after the random walk from 2016 to 2019 suggesting that investor were able to forecast the market returns efficiently in these periods; and(5) previous stock market return has 15% positive relationship, and 0.23 0.23% predictive power. Thus, concludes that the NSM was only become in the weak and inefficient from 2016 up to 2019. As such, the study submits that information security fundamentals should be provided by issuers as at when due for security valuation at no cost.

Time Variation in Cash Flows and Discount Rates

Abstract We analyze the decomposition of the conditional, rather than the unconditional, variance of market returns based on an extension of the standard Campbell–Shiller approach. The relative importance of cash flow and discount rate news in determining the conditional variance of market returns exhibits significant variation over time and relates to economic conditions. The components of the conditional market variance outperform several benchmark variables, including the conditional market variance itself, in forecasting future market returns and realized variance across different horizons. The forecasts based on the conditional market variance components also provide sizable economic benefits compared with benchmark forecasts in an out-of-sample portfolio exercise where a myopic investor allocates her wealth between the market portfolio and a risk-free asset across different holding periods.

Analyst rating matters for index futures

AbstractAnalyst recommendations convey valuable market‐wide information which implies analyst rating should generate reliable predictability for future market returns. This paper examines the predictive regressions which forecast the S&P 500 index futures return and volatility with lagged text‐based analyst rating index (TAR index). Empirical evidence shows that the TAR index generates superior in‐sample predictability. This substantial predictability remains after controlling the business cycles, macroeconomic factors, and economic conditions. Also, the TAR index outperforms the prevailing mean out‐of‐sample and generates significant economic performance. Notably, the TAR index also delivers consistent predictive gains on the volatility of index futures returns.

Business Cycle Variation in Short Selling Strategies: Picking During Expansions and Timing During Recessions

AbstractWe present evidence that short sellers alternate between stock picking during expansions and market timing during recessions. First, firm-level short interest is a much stronger negative predictor of the cross-section of stock returns during expansions than it is during recessions. High short interest also only predicts negative future earnings announcement returns during expansions. We attribute these findings to short sellers’ emphasis on collecting firm-specific signals. Second, short sellers appear to make factor bets more so during recessions than during expansions. These bets tend to pay off as we observe a strong negative relation between the betas of highly shorted stocks and future stock market returns, a result that disappears during expansions. Together, these findings are consistent with theories of information acquisition under attention constraints, endogenous information production, as well as theories of time variation in aggregate overconfidence amongst traders.

More predictors of the investment opportunity set in the ICAPM

To derive the discrete-time version of the Intertemporal CAPM (ICAPM), Campbell (1993) summarizes the investment opportunity set determining investor’s utility as the return to aggregate wealth, and uses an aggregate stock market return to proxy for the aggregate wealth return. In doing so, state variables can be identified as the variables that have forecasting power for future stock market returns. If the stock market return is a true sufficient statistic for investment opportunities, then we should expect any predictor of stock market returns would command a significant risk premium in the cross-section. To verify his theory, we employ 40 predictors of stock market returns from the literature, and see if the risks to these predictors are well priced. We find that exposures to 27 out of the 40 predictors generate significant risk premiums. Given the noise components in the predictors, this finding suggests that the Campbell’s (1993) ICAPM choosing the stock market return as a representative measure for investment opportunities is overall satisfactory.

Mean reversion in a price-earnings ratio and under / overvaluation in the Brazilian stock market

ABSTRACT The market price-earnings ratios differ from those of each share. Despite allowing for several pertinent analyses, authors have rarely addressed these valuation ratios in the Brazilian context. We can use it to evaluate whether the stock market is overvalued (undervalued). In this article, we analyze the mean reversion in a price-earnings ratio based on Ibovespa and identify periods of overvaluation (undervaluation) in the Brazilian stock market. We considered the period from December 2004 to June 2018. Until then, there are no studies that sought to identify periods of overvaluation (undervaluation) in this market. In the analyses, we used non-linear econometric methods. We analyzed the mean reversion in the price-earnings ratio using a unit root test that incorporates a Fourier function in the deterministic term. We identified the periods of market overvaluation (undervaluation) through the regime probabilities obtained from a Markov Switching model, estimated with the price-earnings ratio. The results evidenced that the price-earnings ratio based on the Ibovespa has a non-linear trend and exhibits mean reversion. Thus, this valuation ratio should provide information on the future stock market returns, mostly when it is very dispersed in relation to historical standards. We identified four periods of market overvaluation interposed with five periods of market undervaluation. Mean reversion in the price-earnings ratio contraposes the Efficient Markets Hypothesis. There are no other applications of unit root tests with a Fourier function in the Brazilian context. Furthermore, adopting a Markov Switching model to identify periods of market overvaluation (undervaluation) consists of a methodological contribution. Investors can take advantage of the identification of these periods to establish investment strategies.