Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Testing excess returns on event days: Log returns vs. dollar returns

The results of academic and practitioners’ event studies are often translated from excess log returns into excess dollar returns. The prior literature argues for a difference between the statistical significance of excess log returns and that of excess dollar returns. In contrast, we show analytically and using simulations that specifying event study hypotheses in terms of excess dollar returns is equivalent to specifying them in terms of excess log returns. The prior literature’s result was due to a bias in the estimator of expected excess dollar returns, an incorrect assumption that it is approximately normally distributed, and a misapplication of the delta method.     

Seasonal variations in the U.S. Stock Market returns: 1927–1984

When the seasonal components of the monthly returns as opposed to the returns themselves, are examined over the 1927–1984 period, the Standard & Poor's 500 Composite Index (S&P 500) and the Center for Research in Security Prices (CRSP) value-weighted portfolio exhibit significant seasonality. Their seasonal behavior is quite similar to that of the smallest quintile of New York Stock Exchange (NYSE) stocks and the CRSP equally weighted portfolio during March through October. While January is strong for the two latter portfolios, December, November, and January appear to be consistently strong for the two former portfolios. The seasonal pattern has, however, changed substantially over time. While June and July returns experienced a significant drop in seasonal strength, March and April returns gained seasonal strength for all four portfolios from 1927–1958 to 1959–1984. These changes coincide in an inverse fashion with the shifts in interest rate seasonality.     

Market Efficiency and the Returns to Simple Technical Trading Rules: New Evidence from U.S. Equity Market and Chinese Equity Markets

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. This study is an attempt to explore whether some forms of technical analysis can predict stock price movement and make excess profits based on certain trading rules in markets with different efficiency level. To avoid using arbitrarily selected 26 trading rules as did by Brock, Lakonishok and LeBaron (1992) and later by Bessembinder and Chan (1998), this paper examines predictive power and profitability of simple trading rules by expanding their universe of 26 rules to 412 rules. In order to find out the relationship between market efficiency and excess return by applying trading rules, we examine excess return over periods in U.S. markets and also compare the excess returns between U.S. market and Chinese markets. Our results found that there is no evidence at all supporting technical forecast power by these trading rules in U.S. equity index after 1975. During the 1990s break-even costs turned to be negative, –0.06%, even failing to beat a buy-holding strategyin U.S. equity market. In comparison, our results provide support for the technical strategies even in the presence of trading cost in Chinese stock markets.     

Overreaction of country ETFs to US market returns: Intraday vs. daily horizons and the role of synchronized trading

In this paper we study the intraday price formation process of country Exchange Traded Funds (ETFs). We identify specific parts of the US trading day during which Net Asset Values (NAVs), currency rates, premiums and discounts, and the S&P 500 index have special effects on ETF prices, and characterize a special intraday and overnight updating structure between these variables and country ETF prices. Our findings suggest a structural difference between synchronized and non-synchronized trading hours. While during synchronized trading hours ETF prices are mostly driven by their NAV returns, during non-synchronized trading hours the S&P 500 index has a dominant effect. This effect also exceeds the one that the S&P 500 index has on the underlying foreign indices and suggests an overreaction to US market returns when foreign markets are closed.     

Volatility returns with vengeance: Financial markets vs. commodities

To assess how financial markets and commodities are inter-related, this paper introduces a ‘volatility surprise’ component into the asymmetric DCC with one exogenous variable (ADCCX) framework. We develop an econometric model in which returns and volatility allow to influence pairs of assets, and derive several case studies linking commodities to stocks, bonds and currencies from 1983 to 2013. The innovative feature of our model is that these volatility spillovers are modeled consistently within the correlation dynamics of the ADCCX. We find evidence that return and volatility spillovers do exist between commodity and financial markets and that in turn, their relative impact on each other is very substantial.     

Liquidity and capital under uncertainty and changing market sentiment: A simple analysis

This study introduces a general approach to investigate resource allocation and asset prices in an economy with uncertainty and shifts in market sentiment. The approach presents a number of key features: first, it proposes a choice-theoretic model that determines the utility that the agents derive from holding assets with different liquidity. Second, it incorporates a variable (endogenously-determined) cost structure of asset liquidation, which reflects the (in)efficiencies of the financial infrastructure and changes in market moods. Third, it also incorporates a model of expectations formation under uncertainty and changing market sentiment. While rich in structure, the approach offers a simple analytical framework to investigate resource allocation decision and asset price dynamics under various sources of uncertainty, and to explore the micro-economics of speculative bubbles and boom–bust sequences. The use of a possible market-specific prudential policy tool is discussed.