Karyl B. Leggio

A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

Evidence exists that emerging market stock returns are influenced by a different set of factors than those that influence the returns for stocks traded in developed countries. This study uses artificial neural networks to predict stock price movement (i.e., price returns) for firms traded on the Shanghai stock exchange. We compare the predictive power using linear models from financial forecasting literature to the predictive power of the univariate and multivariate neural network models. Our results show that neural networks outperform the linear models compared. These results are statistically significant across our sample firms, and indicate neural networks are a useful tool for stock price prediction in emerging markets, like China.

An empirical examination of the effectiveness of dollar-cost averaging using downside risk performance measures

Some studies find the dollar-cost averaging investment strategy to be sub-optimal using a traditional Sharpe ratio performance ranking metric. Using both the Sortino ratio and the Upside Potential ratio, we empirically test four investment strategies for alternative asset investments. We find the relative ranking of dollar-cost averaging remains inferior to alternative investment strategies. (JEL G1, G11, N2)

The three-factor model and artificial neural networks: predicting stock price movement in China

Since the establishment of the Shanghai Stock Exchange (SHSE) in 1990 and the Shenzhen Stock Exchange (SZSE) in 1991, China’s stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of Chinese stock. This gap in the literature is significant, given the volatility of the Chinese stock markets and the added risk that arises from the Chinese legal and regulatory environment. In this paper we address this research gap by examining the predictive ability of several well-established forecasting models, including dynamic versions of a single-factor CAPM-based model and Fama and French’s three-factor model. In addition, we compare the forecasting ability of each of these models with that of an artificial neural network (ANN) model that contains the same predictor variables but relaxes the assumption of model linearity. Surprisingly, we find no statistical differences in the forecasting accuracy of the CAPM and three-factor model, a result that may reflect the emerging nature of the Chinese stock markets. We also find that each ANN model outperforms the corresponding linear model, indicating that neural networks may be a useful tool for stock price prediction in emerging markets.

Using weather derivatives to hedge precipitation exposure

Purpose - The purpose of this study is to demonstrate the use of weather derivatives to hedge firm exposure to previously unmanageable risk events caused by natural phenomenon such as excessive rainfall.

Hedging gas bills with weather derivatives

Natural gas company managers concerned with customer satisfaction attempt to minimize the occurrence of extreme bills. Previously, only price fluctuations were addressed with derivative instruments; exchange-traded weather derivatives present a means of hedging exposure to increases in quantity of gas demanded during colder than expected winter months. We model a natural gas company’s ability to adjust for consumer sensitivity and exposure to extreme bills with the use of an optimal mix of weather derivatives and gas pricing derivatives. We find consumer exposure to extreme bills is minimized when the utility uses pricing and weather derivatives.(JEL G11, L51)

Does loss aversion explain dollar-cost averaging?

Abstract Some studies find the dollar-cost averaging investment strategy to be sub-optimal from a mean variance expected utility of wealth perspective. Statman [The Journal of Portfolio Management (1995) fall] introduces a behavioral rationale for the persistence of dollar-cost averaging. Using prospect theory to create an alternative utility function that does not require investors to be strictly risk averse, we empirically test Statman’s conjecture for four investment strategies and for alternative stock investments. We find loss aversion still does not explain the existence of the dollar-cost averaging investment strategy.

Covered Call Investing in a Loss Aversion Framework

In a mean-variance framework, the covered call investment strategy has been seen as an inefficient method of allocating wealth. Covered calls reduce the riskiness of the portfolio and therefore lead to lower portfolio returns. Recent debate has focused on the shortcomings of mean-variance efficiency as an accurate depiction of investor utility. Using alternative utility functions, we find mixed support for the use of the covered call investing strategy. Using loss aversion, however, we reexamine the covered call investment decision and find it significantly enhances investor utility relative to an index portfolio investment strategy. We conclude that loss aversions more accurate depiction of investor preferences and behavior helps to explain the popularity of the covered call investment strategy.

Executive Decision-Making under KUU Conditions: Lessons from Scenario Planning, Enterprise Risk Management, Real Options Analysis, Scenario Building, and Scenario Analysis

Publisher Summary This chapter provides overviews and comparisons of major concepts as well as methodologies from the fields of finance and strategic management. This chapter draws on the field of finance for Enterprise Risk Management (ERM), Real Options Analysis (ROA), Scenario Building (SB), and Scenario Analysis (SA). These techniques and processes are compared to Scenario Planning (SP), a concept, and methodology from strategic management. SP is a strategic management methodology used extensively by senior executives since its application at Royal Dutch Shell in the late 1960s and early 1970s. ERM is a broad approach that has recently become more pervasive in use with the increasing emphasis on improving governance processes in companies. SB is a decision support tool for developing quantitative or qualitative descriptions of alternative outcomes. SA, a sub-set of SB used in finance and accounting, is a means of establishing internally consistent sets of quantitative parameters used as inputs for modeling investment alternatives. Additionally, this chapter draws on the KUU (Known, Unknown, and Unknowable) framework to demonstrate the commonalities and differences among these approaches and calls for their synergistic use.

Efficiency Measurement in: Branch Bank Service with Data Envelopment Analysis

Banking is a competitive market since the industry deregulated. Consumers’ demand for convenience services led to an increase in the number of branches to serve a geographically dispersed customer base. Increasing the number of branches indiscriminately is not advisable since more branches increase the cost structure for the bank. Banking executives can evaluate the relative efficiency of branches, segments, and markets using analytical tools such as DEA. This research assists the branch manager with understanding the efficiency of branches and segments using two alternative intermediation models and a profit model. The results show that large markets are more efficient than small or rural markets, and large segments are generally more efficient than small market segments. The methodology employed in this study allows branch managers to proactively work to improve efficiency and control costs by adjusting inputs within the manager’s control, whether efficiency is measured based on profit models or the intermediation models.

Assessment of Credit Risk Models on Rule 144A Corporate Bonds

Accurate assessment of credit risk can improve the performance of bond portfolio managers. Using credit ratings and market-based credit risk models from S&P and Bloomberg, we investigate the performance of four credit risk models in the Rule 144A corporate bond markets in the United States over the 1990–2015 period. The authors divide their sample into straight bonds and convertible bonds and find that (1) when it comes to straight bonds, discrete models such as S&P’s credit ratings and Bloomberg ratings determine yields more accurately than the continuous market-based models of S&P and Bloomberg; (2) with regard to convertible bonds, a convertible option has a stronger effect than credit ratings in determining yields, and only Bloomberg default risk ratings, not S&P credit ratings, determine the yields; (3) for convertible bonds, the continuous market-based models of S&P and Bloomberg affect yields more significantly than discrete models; and (4) when it comes to predicting actual defaults, Bloomberg models are superior to S&P’s models, and the Bloomberg discrete model has more power than its continuous counterpart.