Excess Returns Research Articles

Benchmarking asset pricing models in emerging markets: evidence from Borsa Istanbul

PurposeInvestors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.Design/methodology/approachOn 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.FindingsOur empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.Practical implicationsIndividuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.Originality/valueThe study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).

The Fama-French Three Factor Model Test on Excess Stock Return: Evidence From Hong Kong, Indonesia and Singapore Capital Market

The presence of the capital market has a very important role in the world economy because the capital market carries out economic and financial functions. One of the instruments in the capital market is stock. In stock investment, investors expect a return with a size that is in accordance with the level of risk they can afford. This study wants to test whether the Fama-French Three Factor Model variables, namely market excess returns, book-to-market equity and firm size, can be used to explain excess returns on the stock markets of Hong Kong and Indonesia with the observation period of 2018–2022. Based on the test results multiple linear regression, Gibbons, Ross, and Shanken test (GRS test) and spanning test, this study found that the variables in the Fama-French Three Factor Model can simultaneously be used to explain the excess return on all stock portfolios on the stock market in Hong Kong and Indonesia, and can only be used to explain the excess return on Big and S-H portfolios in Singapore. Based on the results of the GRS-test, in the Hong Kong market the most efficient portfolio is S-M, in the Indonesian market the most efficient portfolio is the Big portfolio, while in the Singapore market all portfolios are efficient and based on the spanning test, in the Hong Kong market the potential redundant factor is SMB, in in the Indonesian market, the potential redundant factor is HML, while in the Singapore market, there is no redundant potential factor.

Unveiling the timeless potential: A comprehensive analysis of ESG integration in Chinese listed firms

This paper provides a practical guide for integrating Environmental, Social, and Governance (ESG) information into portfolio construction in Chinese listed firms. We analyze panel data from 2015 to 2019, revealing that higher ESG scores correlate with increased stock market returns. We construct an ESG portfolio, identifying strategies that affirm ESG's value. Notably, our enhancement portfolios for both CSI 300 and CSI 500 consistently outperform their benchmarks. Through Brinson attribution analyses, we attribute the excess return primarily to individual stock selection. Our findings underscore the incremental value of incorporating ESG information in stock price explanations.

Global natural rates in the long run: Postwar macro trends and the market-implied [formula omitted] in 10 advanced economies

Benchmark finance and macroeconomic models appear to deliver conflicting estimates of the natural rate and bond risk premia. This natural rate puzzle applies not only in the U.S. but across many advanced economies. We use a unified no-arbitrage macro-finance model with two trend factors to estimate the natural rate r∗ for 10 advanced economies. We cover a longer and wider sample than previous studies and draw on new sources to construct yield curves and excess returns. The two-trend model improves the explanatory power of yield regressions and return forecasts. Most variation in yields is due to the macro trends r∗ and π∗, and not bond risk premia, which are flatter than previous estimates. Our r∗ estimates covary with growth and demographic variables in a manner consistent with theory and previous findings.

Cross-asset momentum and the hybrid fund transmission mechanism in China's stock and bond markets.

The allocation of assets across different markets is a crucial element of investment strategy. In this regard, stocks and bonds are two significant assets that form the backbone of multi-asset allocation. Among publicly offered funds (The publicly offered funds in China correspond to the mutual funds in the United States, with different names and details in terms of legal form and sales channels), the stock-bond hybrid fund gives investors a return while minimizing the risk through capital flow between the stock and bond markets. Our research on China's financial market data from 2006 to 2022 reveals a cross-asset momentum between the stock and bond markets. We find that the momentum in the stock market negatively influences the bond market's return, while the momentum in the bond market positively influences the stock market's return. Portfolios that exploit cross-asset momentum have excess returns that other asset pricing factors cannot explain. Our analysis reveals that hybrid funds play an intermediary role in the transmission mechanism of cross-asset momentum. We observe that the more flexible the asset allocation ratio of the fund, the more crucial the intermediary role played by the fund. Hence, encouraging the development of hybrid funds and relaxing restrictions on asset allocation ratios could improve liquidity and pricing efficiency. These findings have significant implications for investors seeking to optimize their asset allocation across different markets and for policymakers seeking to enhance the efficiency of China's financial market.

How Much Can Machines Learn Finance from Chinese Text Data?

How much can we learn finance directly from text data? This paper presents a new framework for learning textual data based on the factor augmentation model and sparsity regularization, called the factor-augmented regularized model for prediction (FarmPredict), to let machines learn financial returns directly from news. FarmPredict allows the model itself to extract information directly from articles without predefined information, such as dictionaries or pretrained models as in most studies. Using unsupervised learned factors to augment the predictors would benefit our method with a “double-robust” feature: that the machine would learn to balance between individual words or text factors/topics. It also avoids the information loss of factor regression in dimensionality reduction. We apply our model to the Chinese stock market with a large proportion of retail investors by using Chinese news data to predict financial returns. We show that positive sentiments scored by our FarmPredict approach from news generate on average 83 basic points (bps) stock daily excess returns, and negative news has an adverse impact of 26 bps on the days of news announcements, where both effects can last for a few days. This asymmetric effect aligns well with the short-sale constraints in the Chinese equity market. The result shows that the machine-learned prediction does provide sizeable predictive power with an annualized return of 54% at most with a simple investment strategy. Compared with other statistical and machine learning methods, FarmPredict significantly outperforms them on model prediction and portfolio performance. Our study demonstrates the far-reaching potential of using machines to learn text data. This paper was accepted by Kay Giesecke, finance. Funding: This study was supported by the National Natural Science Foundation of China [Grants 71991471, 71991470, and 72204049], the National Key Research and Development Program [Grant 2020YFA0608604], the Shanghai Pujiang Scholar Project [Grant 21PJC010], the Shanghai Science Project [Grant 23692119300], and the China Postdoctoral Science Project [Grants 2019M650076 and 2020T130107]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01468 .

Instantaneous volatility of the yield curve, variance risk premium and bond return predictability

This paper proposes a new way of estimating the instantaneous volatility of fixed income securities using derivatives data, which can further be used to construct the corresponding yield curve variance risk premium (VRP). We show that this VRP measure exhibits strong long-horizon predictive power for bond excess returns. After controlling for the shape of the yield curve, the VRP strongly predicts 1-year holding period excess returns for 2-year to 10-year zero coupon bonds. The marginal R2 of VRP is as high as 12.6%. One standard deviation increase in the VRP is associated with 2.224% increase in the bond excess return. This result is robust when we include various other bond return predictors, such as the Cochrane–Piazzesi “tent-shaped” factor. The out-of-sample analysis suggests that this predictability is not only statistically significant, but also can be translated into economic gains. Additional tests suggest that this predictability varies with economic conditions.

Can Intraday Return Reversals Predict Future Stock Returns?

Based on the research of the US stock market, it is found that the fierce long-short battle between heterogeneous investors with different views is manifested as the high-frequency inversion between overnight returns and daytime returns over a period of time. This kind of long-short battle has an impact on asset prices over time, resulting in pricing anomaly on a cross-section. Based on the research of A-share market, this paper finds that after controlling the characteristics of scale, book-to-market ratio, reversal effect, profitability, turnover rate, etc., the abnormal frequency of opening high and closing low (AB_NR) is significantly positively correlated with the future return of stocks. The hedge portfolio constructed according to AB_NR has excess returns in time series (opening high and closing low anomaly) which cannot be explained by the existing asset pricing model. Further testing shows that the anomaly is significant in the period of high arbitrage cost and high investor sentiment. The empirical results show that the anomaly is due to mispricing rather than risk premium.

GPT's idea of stock factors

We amalgamate the capabilities of the GPT-4 computational model with the avant-garde methodology of autonomous factor generation, culminating in the synthesis of high-return factors within the equity investment milieu. Empirical outcomes elucidate that the factors conceptualized by ChatGPT attain a commendable Sharpe ratio peaking at 4.49, accompanied by an annualized return reaching 66.16%. Notably, the superlative excess returns garnered remain unaccounted for by the quintessential five-factor model. Through the implementation of an unembellished model averaging paradigm, the ensemble of 35 factors, conceived by ChatGPT, manifests an apex long-short annualized return of 88% and a Sharpe ratio registering at 2.46. In stark contrast to conventional data mining techniques, the temporal expenditure requisite for GPT's factor generation is minuscule. It relies on knowledge inference without the need for data input, and it can provide a thorough economic explanation for its factors.

Media sentiment and stock returns

Based on 35,344 news articles published in the Financial Times that cover 40 companies that have been included in the Dow Jones Industrial Average, we find that a negative media sentiment in the form of a negative language tone in news articles is a priced factor in five of nine asset-pricing models that aim to explain the cross-section of stock returns. In particular, the sentiment factor is a priced factor in the market model augmented with the sentiment factor in all three samples—the 2005–09 subsample, the 2010–18 subsample, and the 2005–18 full sample—and in the Fama-French three- and five-factor models augmented with the sentiment factor in the 2010–18 subsample. In addition, factor-spanning regressions with the Fama-French five-factor model as the right-hand-side model confirm that the sentiment factor contributes to the model’s explanation of the stocks’ mean excess returns in the 2005–09 subsample and the 2005–18 full sample.

The pre-FOMC announcement drift: short-lived or long-lasting? Evidence from financial and volatility markets

ABSTRACT We examine the effects of Federal Open Market Committee (FOMC) announcements accompanied by press conferences (PC) on both financial and volatility markets. We find a pre-FOMC announcement drift, signifying positive excess returns on equities prior to the FOMC announcement. However, the influence of the FOMC-PC announcements is short-lived, becoming insignificant shortly after the disclosure. This contrasts with findings from Lucca and Moench (2015), who report that the FOMC announcement’s impact is persistent (or long-lasting) in their studied timeframe. Furthermore, we identify a significantly negative return of the VIX both before and after the FOMC announcement. This contrasts with the transient nature of stock returns, suggesting an evolving relationship between stock returns and VIX movements. To exploit the FOMC announcement drift, we construct a profitable trading strategy but note that such coexistence of the profitable trading strategy with the short lived impact by FOMC announcement raises a concern whether FOMC justifies a large price movement around FOMC announcement.

Predicting the equity premium with financial ratios: A comprehensive look over a long period in Korea

This study investigates whether financial ratios, such as the dividend-price, price-to-book, or price-earnings ratios, reliably predict the equity premium in Korea. We construct the longest possible time series data for the stock market index excess returns including dividends and associated financial ratios by collecting data from various sources including data only available in hard copy publications from 1968 to 2021. We also construct the cyclically adjusted price-earnings (CAPE) ratio as a predictor which has not been used in previous studies on the Korean stock market. We estimate the predictive regressions using the IVX method, which is robust to having a persistent independent variable and overlapping data as a dependent variable and can accommodate multiple independent variables. We also assess the economic gains of the predictability by examining the certainty equivalent return for a mean-variance investor. We find that the CAPE ratio reliably predicts the equity premium for horizons ranging from one month to two years, and the results survive various robustness tests.

Demand-and-supply imbalance risk and long-term swap spreads

We develop and test a model in which swap spreads are determined by end users' demand for and constrained intermediaries' supply of long-term interest rate swaps. Swap spreads reflect compensation both for using scarce intermediary capital and for bearing convergence risk—i.e., the risk spreads will widen due to a future demand-and-supply imbalance. We show that a proxy for the intermediated quantity of swaps—dealers' net position in Treasuries—flipped sign during the Global Financial Crisis when swap spreads turned negative and that this variable predicts the excess returns on swap spread trades. Exploiting our model's sign restrictions, we identify shifts in demand and supply and find that both contribute significantly to the volatility of swap spreads.

A sharing rule for multi-period interest-sensitive insurance contracts

As sales of interest-sensitive products grow in insurance markets, determining a sharing rule for allocating investment returns between a policyholder and an insurer is crucial. This paper discusses a theoretical sharing rule for a multi-period contract, reflecting that the insurer’s investment efforts are unobservable and that a stream of information inferring the quality of the efforts exists. Our sharing rule is developed by selecting multi-period excess returns as available information and maximizing the expected utility of the policyholder. The empirical analysis shows that the effect of multi-period excess returns on insurers’ sharing patterns is consistent with the theoretical findings.

Nontraded Sector Growth Risks and Economic Sizes in International Asset Pricing

Output fluctuations in nontraded sectors are an important country-specific risk factor because nontraded outputs are only consumed domestically. In interest rate markets, countries with higher nontraded output growth risks are associated with stronger motives for precautionary savings and lower interest rates. In currency markets, strategies with higher exposures to nontraded output growth risks offer higher average excess returns. Economic sizes play an important role in exacerbating the pricing impact of nontraded output growth risks as it requires larger international trades to mitigate nontraded output risks of larger economies. However, because economic sizes are functions of outputs in equilibrium, output growth risks also explain this role of economic sizes in international asset pricing. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03697 .

A Heuristic Model to Find Optimal or Realistic Target Industry Sectors

Policymakers or prospective investors recognize a continuing need to find target industry sectors to expand or contract for a sustainably healthy economy or investment portfolios. This paper discusses how to apply an optimal portfolio theory to selecting target industry sectors. With the information technology and the analytical approach in mind, this paper provides an operational and workable framework for selecting target industry sectors for an economy. The application incorporates the capital asset pricing model, ways to find excess return to risk ratios, unsystematic risk measures, etc. First, this paper shows a practical approach to finding the optimal weights for target industry sectors. Second, this paper develops a heuristic framework for analyzing the performance of industry sectors to construct realistic mixes of industry sectors.

Short-term Persistence Performance of Equity Mutual Fund Returns: Evidence from India

The present study verifies the short-term persistence performance of equity mutual fund returns. The study considers 47 equity funds' monthly excess returns spanning from January 2000 to December 2019. The study employs prominent asset pricing models such as Jensen (1968) one-factor model, Fama-French (1993) three-factor model, and Carhart (1997) four-factor model to capture the short-term persistence of equity mutual fund returns. The results show that Jensen’s one-factor and Fama-French three-factor models are explaining a better persistence performance in the Indian context.

An interval constraint-based trading strategy with social sentiment for the stock market

Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making.

The sustainability factor in asset pricing: Empirical evidence from the Indian market

This study investigates the feasibility of including the sustainability performance of firms in the asset pricing problem. The data of 500 firms from the NIFTY 500 index are used for this study. The stock prices and financial data are downloaded from the CIME database. The sustainability factor is computed using the ESG scores from the Bloomberg database. In order to test the influence of the sustainability factor, the Fama–French Five-Factor model is extended by including the sustainability factor as an additional factor. The dependent variables are the excess returns on 36 size and book-to-market ratio sorted portfolios, 36 size and operating profitability sorted portfolios, and 36 size and investment sorted portfolios. The impact of the sustainability factor on excess portfolio return is tested using the Fama–MacBeth two-pass regression and the Fama–French methodology. The results show that the price of ESG risk (or ESG risk premium) is positive, indicating that firms with lower ESG performance yield more returns than those with higher ESG performance. About one-third of the portfolios witness the significant impact of the sustainability factor on their returns. However, the insignificant relationship in two third of the portfolios between the sustainability factor and excess portfolio returns conveys that in the Indian market, corporate investors have the flexibility to decide on ESG investment. Smaller firms are exposed to a higher ESG risk, and Firms which do not integrate environmental and social costs into their strategies may bear a higher cost of equity.

Trading behavior by foreign and domestic investors

Purpose- Momentum, a well-known anomaly in financial markets, has garnered considerable attention from both academicians and market participants due to its implications for the market efficiency hypothesis and investment plans of investors. While momentum trading can yield excess returns, it is not a possible strategy for all investors because if one segment of investors employs a momentum trading strategy to profit from their trades, it requires another type of investor to take the opposite position to clear the market. Methodology- I investigate the momentum trading behavior of investors in different segments of Borsa İstanbul. Specifically, I focus on the momentum trading behavior among local and foreign investors and private and institutional investors. Findings- The results show that foreign investors’ stock holdings are positively associated with past returns inferring momentum trading while domestic investors are contrarians. Further analysis of domestic investors’ investing strategies reveals that the contrarian trading of domestic investors is mainly driven by individual investors. Conclusion- The results show that foreign investors’ stock holdings are positively associated with past returns inferring momentum trading while domestic investors are contrarians. Also, the contrarian trading of domestic investors is mainly driven by indvidual investors. Keywords: Momentum trading, investor behavior, foreign investor, domestic investor, panel data JEL Codes: G40, G11, G14