Momentum Investment
Predicting the direction of stock markets or examining whether excess returns exist in stock markets is not only the primary concern of market participants but is also a major research topic for academics. An efficient market hypothesis (EMH) was recognized as the core theory of investment until various anomalies in stock markets were found in the 1980s. The EMH states that asset prices fully reflect all available information and that stocks always trade at their fair value, making it impossible for investors to purchase undervalued stocks or sell stocks for inflated prices. On the other hand, the market anomaly proves that due to inefficiency in the process of reflecting information, stock returns follow a certain pattern and provide excess returns. A representative study for identifying expected stock returns is by Fama-French, where a three-factor model is proposed. The three factors incorporated in this model are market risk, the outperformance of small versus large companies (SMB, Small Minus Big), and the outperformance of high book/market versus small book/market companies (HML, High Minus Low). Charhart added a momentum factor to the Fama-French three-factor model and showed linear correlations between the momentum factor and stock returns. A recent study by Ayub et al. also incorporated a momentum factor to propose a new six-factor downside beta capital asset pricing model (CAPM) for asset pricing.
A number of previous studies have focused on momentum investment strategies to sustain competitive advantage for portfolio management. Jegadeesh and Titman demonstrate that a momentum investment strategy of buying a portfolio with good past performance and selling a portfolio with poor past performance can realize significant abnormal returns over a 3- to 12-month holding period. Rouwenhorst analyzed 12 European countries and proved that the momentum investment strategy is effective within a year in developed countries, as well as emerging countries. Griffin et al. analyze emerging Asian markets, as well as the U.S. and European markets, using the momentum investment strategy outlined by Jegadeesh and Titman. The study reports that most countries except Korea, Japan, and Hong Kong have achieved abnormal returns from momentum investment strategies. Moskowitz and Gridblatt show that short-term excess returns from momentum effects could be generated in various portfolios. On the other hand, Aldieri and Vinci explore the relationship between firm size and sustainable innovation in large international firms. They found internal as well as external spillover effects on firms' sizes.
The momentum investment strategy has been a trend-following strategy adopted by many fund managers in the practical field, and these managers have been attempting to improve its performance. Grinold and Kahn use the information ratio (IR) to eliminate
the risk of irregularity in the regime transition pattern in the trend-following strategy. These researchers use the formula of 
, where IR measures active management opportunities and squared IR indicates
the ability to add value. In this instance, IC (Information Cooperative) represents the management skill, and Breadth represents the number of independent opportunity sets, which is the number of times the skill is used. The trend of investment strategies
employed by recent hedge funds is to use a strategy to minimize the risk of regime judgment by increasing the number of independent opportunity sets as much as possible, rather than increasing the IC. Bridgewater Associates, the world's largest hedge
fund, is a prime example. Meanwhile, few efforts to increase IC have been made by practitioners and academics. Researchers have recently reported that scientific methodology can be used to improve the accuracy of transition pattern recognition. The
hidden Markov model (HMM) shows good predictive power for transition pattern recognition. The HMM based on the Markov chain states that one can observe only the results from the hidden states, and the hidden states are only influenced by the present
state and have nothing to do with the past state. Therefore, the HMM is useful for time-series pattern recognition that changes over time. Sasikumar and Abdullah describe a detailed idea about the sequence and state prediction of the stock market
using HMM and address inferences regarding stock market trends. Hassan and Nath employ the HMM for forecasting stock prices for interrelated markets and compare the results with those from an artificial neural network (ANN). A recent study by Nguyen
determines an optimal number of states for the HMM and predicts monthly closing prices of the S&P 500 index using the selected HMM. This study shows that the HMM outperforms the buy and hold strategies in predicting and trading stocks. These researchers
support the use of the HMM and argue that the HMM offers a new paradigm for stock market forecasting.
There has been a growing demand for portfolio management using artificial intelligence (AI). To sustain a competitive advantage for portfolio management, stock market investors require a strategic investment decision that can realize better returns. Based
upon previous finding that the HMM is useful for time-series pattern recognition, this study proposes a momentum investment strategy using the HMM to select stocks in the rising state. We use the HMM
to increase the IC in the formula. We construct an HMM momentum portfolio that includes 890 Korean stocks and analyze the performance over the period from January 2000 to December 2018. Identifying states of stocks, sectors,
or markets using the HMM, we construct an HMM momentum portfolio by purchasing shares in the rising state and proceeding with rebalancing after the holding period. The HMM momentum portfolio is found to earn higher returns than traditional momentum
portfolios and achieves the best performance under certain conditions of the short holding period (one week) and short formation period (one month). In addition, our strategy shows competitive performance in market and sector index investment against
market returns. The experimental results in this study imply that the momentum investment strategy using HMM is useful in the Korean stock market, and the HMM can be used to develop a new AI momentum strategy that can be utilized for other portfolios
containing various types of financial assets on the global market.
A large number of models or techniques for creating an efficient portfolio consisting of various types of instruments have been developed by academics and practitioners. Financial instruments, investment techniques, and investors are critical components in the efficiency of financial markets. An effective investment strategy helps investors to achieve efficient investments as well as an efficient financial market, which are well known to play an important role in sustaining economic growth. Portfolio managers are able to make more efficient strategies by using our HMM momentum investment strategy. It helps to sustain a competitive advantage for portfolio management and contributes to the efficiency of financial markets. In this sense, the HMM momentum investment strategy developed in this paper plays a role in sustaining economic growth.
We organize this paper as follows. Section 2 includes the materials and methods. In this section, we describe the HMM, momentum strategy, and our proposed model. The experimental results and an analysis of the results are presented in Section 3, and Section 4 offers a summary and concluding remarks.