Investment Strategies and Trading Approaches in Behavioural Finance

The behavioral finance literature documents several long-term investment strategies that persistently generate superior risk-adjusted returns. This study focuses on several investment strategies, trading approaches, and problems with the traditional financial theory’s explaining their logic and the historical returns generated by these strategies. In the case of the investor’s sentiment indicators and the anomaly, an exchange-traded fund (ETF) has been created to enable investors to simply and efficiently invest in the strategy.


Introduction
Behavioral finance is a study that analyses the phenomena of the market, keeping in view the psychological factors involved in the behavior of the investors. Behavioural Finance mainly focuses on how investors interpret and act on micro and macro information to make investment decisions. The most common behavior that the investors do when making investment decisions. Investors often do not participate in all asset and security categories, Individual investor's exhibit loss-averse behavior, Investors use past performance as an indicator of future performance in-stock purchase decisions, Investors trade too aggressively, Investors behave on status quo, Investors do not always form efficient portfolios, Investors behave parallel to each other, and Investors are influenced by historical high or low trading stocks. Behavioral biases can occur within a short-term trading strategy or long-term investment time perspective. Graham & Dodd (1934) differentiate the short-term speculation and long-term investment as follows: "An investment operation is one which, upon thorough analysis, promises safety of principal and an adequate return. Operations not meeting the requirements are speculative." In general, trading strategies are more relevant to the competitive hedge fund industry.

Strategies and Behavioural Finance
Investors' risk tolerance may also affect their asset allocation strategies. For example, most investors do not make yearly adjustments to retirement accounts because they suffer from status quo bias (Kahneman et al. 1991). Status quo bias refers to an irrational preference for the current state of affairs. The status quo or current investment posture is taken as a reference point. Any change from the initial reference point is perceived as a loss.
Several studies, such as Malkiel (1995) and Bollen & Busse (2005), find modest evidence showing persistent performance. That is, winning (losing) managers, on balance, keep on winning (losing). If markets are not fully efficient, active management may be a worthwhile activity for those willing to Shanlax International Journal of Management s h a n l a x # S I N C E 1 9 9 0 put substantial amounts of time and rigorous effort into the endeavor. However, any active manager should have a healthy respect for both the difficulty in outperforming an index on a risk-adjusted basis and in overcoming transaction costs. Investors who lack a passion for the financial markets or access to highly knowledgeable and experienced financial advisors are likely best served with index funds.

Scope of the Study
The behavior of the investor is different from one another; each has its perception to develop their portfolios. Every investor is different, with different financial goals. From behavioral finance, these characteristics are often defined more rigorously as objectives and constraints.
However, it is a balance between risk and returns, with each investor having unique requirements, as well as a unique financial outlook. Every investor thought that they must have strategies for investment, but they have trapped due to psychological confront.

Statement of the Problem
The traditional financial theories were based on the framework that investors act rationally and consider all the available information in making the financial decision, which are the major tenants of the efficient market theory. It also assumes that the market is transparent and does not suffer from any information asymmetry. The turbulence faced by the capital market cannot be explained by the efficient market theories since it assumes investors act rationally. The eroding retail investor base is very critical since the capital market requirement contributed by domestic savings can curb the flight of capital. Further, the artificial volatility can be curtailed and controlled with a larger participation of domestic retail investors Objectives of the Study 1. Explain the difference between the traditional and behavioral finance perspective to trading and investing and 2. Identify the problems of investing in traditional investment strategies and behavioral-based Trading Strategies.

The Methodology of the Study
The paper is mainly conceptual, and it is based on the earlier research works, journals, articles related to behavioral finance available over internet-based sources. Various other related books and journals which are available in physical form are also accessed to develop the foundation of the paper.

The Problem's with Traditional Investment Strategies
Many widely accepted academic theories of traditional finance and investment strategies fall short of their promises when implemented over a full market cycle. There are many traditional investment strategies, such as those based on Markowitz (1952Markowitz ( , 1959 portfolio theory, the dividend discount model (DDM), and discusses where each falls short of performance expectations in practice. The inability of these approaches to effectively describe financial reality creates an opening for behaviourally based investment strategies. Markowitz (1952Markowitz ( , 1959, who shared the 1990 Nobel Memorial Prize in Economic Sciences, provides one of the important contributions to finance. He was the first to explicitly measure portfolio risk and fully understood the impact of correlation on the risk of the portfolio, rather than merely the risk of the underlying securities. Markowitz also developed a quadratic program, a substantial mathematical achievement, to solve for what he termed as efficient portfolios (i.e., the investment opportunity set that maximizes return for a given level of risk or minimizes risk for a given level of return).

Problems with Markowitz Portfolio Theory
Although many still use Markowitz's portfolio theory at the asset allocation level, its practicality has limitations at the individual stock selection level for most investor applications. The efficient frontier is unstable. In other words, a recommended portfolio for an investor today may differ markedly from a new optimization for the same investor at a later date. Financial advisors would lose credibility with their clients by recommending major portfolio adjustments on such a frequent basis. The practical application of the Markowitz model, like nearly all models, is based on the quality of its inputs, such as the expected Shanlax International Journal of Management s h a n l a x # S I N C E 1 9 9 0 return and risk as measured by the standard deviation of each security. The most common methods for obtaining estimates all have flaws, such as the capital asset pricing model (CAPM), market model, arbitrage pricing theory (APT), and Fama-French three-factor model. Third, an important flaw with practical applications of the Markowitz model is that correlations often increase close to a perfect positive correlation (+1) during times of economic distress, thus eliminating the benefit of diversification, which is a central assumption of the model. Therefore, the investment principle of diversification often does not provide meaningful help precisely when investors need it most.

Problems with the Dividend Discount Model
The dividend discount model (DDM) states that the price of a stock equals the present value of the future cash flows generated by the asset. Theoretically, its logic is unassailable. Investors know that "time is money" and that they should discount future cash flows back to the present. Yet, applying the model in the real world reveals its shortcomings. Although valuing an asset as the present value of future cash flows works well for bonds, it remains a challenging task for stocks that exist in perpetuity. How can an analyst reliably predict dividends or earnings for a firm out one year? The DDM has three major forms: no growth, constant growth, and irregular growth. Let's look at the constant growth version of the model, often called the Gordon constant growth model, because of its wide use in finance textbooks and the Chartered Financial Analysts (CFA) program.

Short-term Behaviourally Based Trading Strategies Momentum
The success of momentum strategies is one of the most serious challenges to the efficient market hypothesis (EMH). The EMH states that security prices reflect all available information. Hence, consistently beating the market by devising an investment strategy that regularly delivers superior risk-adjusted returns is impossible. A common catchphrase among technical analysts for momentum is that "the trend is your friend." In other words, winning investments keep winning while losing investments keep losing, at least about a market index. Momentum can be measured in many ways, but its calculation typically examines the price of an asset relative to an index. The relative strength and moving average statistics are among the most common statistics used to measure momentum.

Earnings Surprises
An earnings surprise is the difference between the reported earnings per share (EPS) and the consensus sell-side estimate, tracked by First Call, or another sell-side earnings aggregator. The surprise is positive if the reported EPS is greater than the consensus EPS estimate. Conversely, the surprise is negative if the company reports a number less than the consensus. In contrast to the Jegadeesh and Titman (1993) approach on momentum in stock returns, Dreman and Berry (1995) discuss under reaction in the framework of analyst estimates and valuation metrics. In the context of earnings surprises, when news arises, investors are skeptical, so they need time to accept that the news is real or sustainable.

Investor Sentiment Indicators
The term investor sentiment is synonymous with the terms feeling and emotion of investors. These terms differ from purely rational behavior or the EMH. Analysts have created and historically applied several sentiment indicators to the market as a whole, but some may also apply to individual equities. This section discusses some investor sentiment indicators and the behavioral logic related to why they may be of value to investors. Some attribute the put or call ratio indicator to invest legend Martin Zweig, who was best known for "calling" the Crash of 1987. Puts (sell) options enable investors to profit from a decline in the price of an asset, and call (buy) options enable them to profit from a rise in a stock or other asset. A high value of the put or all ratio tends to indicate that investors are excessively bearish, hence perhaps the market may trend upward. Historically, more traders tend to buy call options than put options because the market goes up over the long term, so a value greater than 1 for the put or call ratio is uncommon. Many technicians view the value of the ratio as greater than 0.6 as bullish for equities. A behavioral connection to the put or call ratio is one of overreaction or having Shanlax International Journal of Management s h a n l a x # S I N C E 1 9 9 0 the pendulum of market psychology move too far in one direction of price.

Long-term Behaviourally Based Investment Strategies Value Anomalies
Researchers such as Fama & French (1992) find that value stocks outperform growth stocks. However, investors suffer from behavioral bias that causes them to assume growth stocks have a better historical investment performance than value stocks most of the time. Of course, many ways are available to measure value, such as low price or earnings, low price-to-book, low price-to-cash flow, and high dividend yield. The focus on a specific valuation metric is how this anomaly differs from the momentum and reversal effects discussed earlier.
The Dogs of the Dow strategy provides an intuitive example of the strategy in action and its possibly related behavioral bias.
It is tied to the value anomaly, but the strategy may also be a variant of the "too big to fail" theme that analysts typically apply to banks. Although some former Dow stocks such as Kodak and General Motors fail, the vast majority are large, wellcapitalized, international companies. Jack Welch, legendary former chief executive officer of General Electric (GE), once noted that GE would be number one or two in each industry where it competes. If not, GE will fix or exit the business. Combining these thoughts suggests that although blue-chip Dow companies may suffer from missteps, they can typically recover, and shareholders will ultimately reward these stocks.

Accruals Anomaly
Differences exist between earnings and cash flow. The former is an accounting construct, which is easy to manage; the latter shows the inflows and outflows of cash and is more difficult to manipulate. The reported earnings number relies on management choices of inventory valuation, allowance for doubtful accounts, Accounts payable, accounts receivable, and so forth. A "red flag" exists if a persistent difference occurs between earnings and cash flow over time, after adjusting for expected items such as depreciation, amortization, and changes in working capital. Sloan (1996), who first identified the accruals anomaly, constructs a long or short portfolio with the long (short) portfolio consisting of firms with the least (most) accruals. He finds abnormal returns of roughly 10 percent a year for his long/short portfolio. Sloan suggests that the results are due to overreaction and cognitive errors as a result of Wall Street's fixation on earnings. This anomaly may also be a framing issue because management can fool investors, at least some of the time, by putting the best spin on reported earnings. Currently, several ETFs such as the Forensic Accounting ETF (FLAG) use accounting quality of earnings filters, such as accruals.

Conclusion
Behavioral finance now has broad approval among financial practitioners. However, many in academic finance still are proponents of the traditional finance school. Investment professionals and individual investors argue that securities markets are inefficient in the context of prices and do not always reflect all available information. Achieving a positive alpha for a portfolio remains elusive for most managers, but findings of behavioral finance may provide important clues. They are thriving with findings from the behavioral finance field as the foundation for their strategies. Important anomalies, including momentum, accruals, value, reversals, earnings surprises, and herding, have their roots in behavioral finance. Combining several of these anomalies into an investment strategy is likely the future of active investment management. Unfortunately, a unified theory of behavioral finance is unlikely to fit into a nice, simple CAPM type equation. Successful investment analysts will analyze not only the fundamental and technical aspects of securities but also the behavioral factors that ultimately influence their prices.

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