Why Price Momentum Is Contrary to the Efficient Markets Hypothesis - Assignment Example

Market Efficiency Part A: What ‘price momentum’ is and why ‘price momentum’ appears to be contrary to the efficient markets hypothesis? Vishny (1998) states that price momentum can be described as a phenomenon whereby price of a stock gains ‘momentum’ due to an under or over calculation associated with ‘news’ associated with a stock. It truly represents the ‘surprises’ or ‘shocks’ that are all true sudden for example a sudden news in the market about expected losses that a firm booked can lead to price momentum in a downward shift, or otherwise in the case if considered the other way around. The price momentum may be an outcome associated with the failure to understand and incorporate the forecasted trends of shock-earnings into pricing. This also derives from the fact that the profits, mainly, associated with a stock are from the capital gains aspect, rather than dividends, which is why the term ‘price’ and ‘momentum’ are used simultaneously. Also, the cash flow shocks, if embedded within the pricing, the price momentum can be observed. Or in a generalized manner, embedding any shock in the stock pricing implies the presence of price momentum. Shivakumar (2006) agrees that this phenomenon does seem contrary to the efficiency market hypothesis, whereby, the hypothesis state that information is readily and equally available to all investors to ensure that the decision making of each is the differential amongst their strategy, because strategy is derived from information on which a decision is made. This concept is also agreed upon by Subrahmanyam (1998), Fama (1998) and Martin (2003) during their analytics on this model. Along the similar lines, if information available to everyone is the same, then there is consistency of information availability in the market. Thus, a competitive environment shall prevail. However, this phenomenon exists in idealistic situations only, and on a general note, factors such as insider-trading, using privileged information and so on do exist in markets globally. Subsequently, there are shockers – shocking news in the market – that prevail and the price of a stock fluctuates according to these shocks that can either be negative or positive; the former slides stock prices down, and the later carries it up, and the force that takes it up or down is known as the ‘price momentum’. Additionally, if the assumption of efficient market hypothesis would prevail, then equal information shall be available to all, and there would be presence of ‘shock absorbers’, because since an information would not just be available for certain individuals but for everyone, therefore, there would be not much of a shock for people to know about the case. MacKinlay (1990) states an example that assume a case where the CEO of a firm died over night, and was mainly responsible for the expansion and a lot of great job that he did and now the firm lacked leadership. In case of an efficient market, everyone would know this, and therefore, there wont be a rush to sell off the shares because there wont be anyone to buy them as the firm would be in trouble, other than the opportunist. So a sensible investor would retain the shares and let the market float, till some positive news comes and then shares can be sold. In case the market is not efficient, this news would be restricted to few individuals who would sell off their shares as soon as they can and let other investors sink as the news wont have been public by then, and this quick-sell from certain investors and then the shock would eventually lead towards the sliding price momentum. Theoretically, as stated by Titman (1993), in case of efficient financial markets, there won’t be an excess profit margin for investors, thus illustrating that price momentum is a compensation for bearing the risk associated with the awareness or unawareness of investors. Therefore, it is very visible that the hypothesis of efficient markets is much contrary to the phenomenon of price momentum. Part B: The alternative explanations for ‘price momentum’. Hameed (2004) states that in the early 90s, the Price Momentum model was introduced as a measure or indicator of the short term price performance that led to identification of the stocks that would be performing well in the short term foreseeable future. Mainly based on the historical archive data, the model looked at the stock performance of the past twelve months, short listing the stocks on the basis of their performance in the most recent quarter, and in particular the last month. The results have been found to be most optimal when used in the time horizon of a single quarter as mentioned. The model utilizes the long term as well as the short term fluctuations and price movements, in the form of twelve months and the three months terms, to develop the price momentum. Scores are on the higher side for the stocks that have very strong performance in the term of twelve months and also depicting a price consolidation in the past three months, and more specifically in the past one month. Hameed (2004) further describes that the model mainly has two scores for price momentums in its database. Firstly is the PRM (Raw Price Momentum), and its values range between negative to positive 998 i.e. -998 to +998; amongst these values, the extremes are considered of value, while the zeros indicate neutral levels. The second score is the PMO (Price Momentum Percentile) provides a ranking from 100 to 1 that is fundamentally based on the PRM score, the details of which have already been mentioned. On the basis of this scale, if a share gains a value of 70 or above, it is then recommended as ‘buy’, while a value of 30 or below, makes it eligible for ‘sell’ recommendation. It is noticeable that as per the guidelines of this model, the twelve months price performance is mandatory, and if not available, then there will be no price momentum score available or calculations therewith. Following are some of the explanations of price momentum, other than those stated above, and their brief evaluation. Zhang (2006) states that price momentum is the momentary movement of the stock price towards positive or negative stance, whereby, the flow is pushed by means of an event that actually triggers this momentum and keeps it going till another event intervenes and halts the previous momentum. This new event can cause the previous momentum effect to enhance, change its direction or have it completely halt. The even triggering a price momentum can have a variety of resources such as impact of business environment, legalities of the business, any major fluctuation in prices or demand and supply, any major announcement by the government or the firm itself, and so on. This explanation of price momentum is precisely in line with the previously defined meaning of it. Vishny (1998) states that price momentum can also be determined via modeling across a good spam of time that is ample enough to understand the movement of the stock prices of a particular firm or a sector. In this form of modeling, the weightage of each of the year may vary considering various factors such as understanding the seasonal variants, deploying more weights to current periods, and so on. This would present a classical model on how the pricing momentum would move about, its expected direction and anticipated values. Logical does agree to this definition of price momentum though not precisely in line with the earlier described wordings. It is true that trend analyses do depict a good picture of the past, and trend analysts do believe that forecasting the future based on past values and trends is a classical art. Considering these factors, it can be suggested that price momentum does reflect from the historical performance and trends as settled in the past. In accordance with Subrahmanyam (1998), price momentum is defined as the rational for profit making and for profit makers, or any investor for that matter to stay in the market for any sustainable period of time. Various researches reveal that it’s the capital gain that truly attracts an investor to invest in any financial market, as investors, generally, do not look for dividend gains for several reasons, but the major factor being time associated with the ‘wait’ and the fact that stock prices are adjusted automatically for their dividends. So ultimately the stand point becomes the same as it was prior to the dividend, in fact, the differential remains of the time that the investor has to hold the share, and holding the share involves not just opportunity cost but risk as well. Following the same, the investor priority is to see the price momentum, implying sell and buy if the momentum is sliding down or buy and sell if the momentum is moving the price up. In the real sense, it is truly the price momentum that drives up or down the investor profits by all means. Pricing momentum has been identified by Titman (1993) as a single variable that has distinguished the winners over the losers in the set of trends set for twelve months or so. In the similar regard, various experiments were conducted that proved this to be right, and the pricing momentum stood along with other variables that were in common for the winners and the losers amongst themselves. On a general note, price momentum is discussed in two ways i.e. about buying and selling times; If the trend states that the past 3-12 months have depicted higher returns, then the same can be projected over the next 3-12 months If the trend states that the past 3-12 months have illustrated a poor growth, then the same can be projected over the next 3-12 months. Titman (1993) further states that the price momentum model is strictly based on the assumption that stock markets are not efficient – as explained previously, price momentum is contrary to efficient market hypothesis. This assumption has been found quite valid by economists, and thus, the basis of the model is assumed correct. There are two explanations that illustrate the performance and practicality associated with this model, and these are: Investors tend to take advantage of the nature of human beings i.e. to over-react to a given situation or circumstance, alongside the ‘herding’ mentality phenomenon. Investors who tend to use price momentum model as their course of action are subject to an added degree of risk compared to those who work out otherwise. Subsequently, they need a higher rate of return to compensate the added degree of risk undertaken. The key terms identified during this research from various analyses are: Over/under reaction Risk explanation Data mining Since the model is under consideration, therefore, it becomes essentially critical to analyze these key terms in a brief, yet appropriate manner. Reaction implies the response of an entity to a course of action. In terms of stock, the reaction of the market or a stock is considered as a response to an event or action that takes place. This derives from the theories that state markets to be sentiment driven. Mainly, analysts and investors anticipate the market to respond in a certain manner, in response to an event. However, at times, the actually response or reaction is lesser in velocity compared to the anticipated stance; this is known as under reaction. While at times, the opposite happens i.e. the magnitude of reaction is higher than the anticipation. In such a scenario, it is stated as an over-reaction. However, this reaction is short lived, and theories state that in the long run, the anticipated reaction is what actually prevails. Therefore, this becomes a major point of concern for many investor as they look for strategies for taking stance of buy or sell in case of under or over reaction respectively. In either of the case, price momentum comes into play as the magnitude of over or under reaction is determined by means of the price momentum. MacKinlay (1990) states that playing on such information and analyses bear more risk than the usual market risk and thus, this additional risk is compensated by the added gains over and above the market returns. This is the finding associated with the risk held in holding such securities MacKinlay (1990) further states that data mining is a critical phenomenon in this regard; in simplest terms, data mining is about digging out data from a single or various data banks or sources. Since data of past months is needed, therefore, there is increased demand for data mining for price momentum workings. In accordance with Shivakumar (2006), the presenters of this model, Jegadeesh and Titman, examined various strategies associated with trading, and concluded that during the period of 1956 and 1989, the successful investors were those who actually went to buying the past winners and sold off the past losers, comparing the numbers from 1956 to 1989. Essentially, this remains more valid for the 6 months prior numbers, projected on the next 6 months. The simplest formula that has been developed for calculation of price momentum is: M = CP – CPn Whereby M is the momentum CP is the current period’s closing price CPn is the closing price N period ago Furthermore, Shivakumar (2006) states that an important variant of this model is the indulgence of moving averages into this formula; analysts inter-mingle moving averages into the price momentum model to refine it further for giving the most accurate forecast on the momentum. In this variant, analysts take a serious of price calculations and plot them alongside the moving average of the momentums. Buying is the call on this model when the momentum of pricing moves above the averages, and makes a stay in that graphical region for a few trading sessions. On the contrary, sell is the call when the momentum of the pricing moves below the averages. As stated in the initial part of this assignment that pricing momentum is solely based on past performances of a stock and has forecasts based on the historical prices, therefore, the approach is more commonly used by technical analysts rather than the fundamental analysts, because the later are of the opinion that the pricing of a stock is based on its ‘intrinsic value’. However, a variant of the price momentum model is also available for the fundamental analysts, known as Contrarian Investing Strategy, whereby, in a case, a fundamental analyst would believe that a stock that has been rising is probably overvalued, while the one falling is quite possible under-valued. Following this strategy, the fundamental analysts can determine whether or not the stock is under or overvalued and derive the underlying possibility of any profits therein. Alongside, the momentum of a firm’s financial performance may also lead to the determination, whether or not the stock is priced for profits or called for selling. References Books 1. Barberis, N., Shleifer, A. and R. Vishny (1998), ‘A model of investor sentiment’, Journal of Financial Economics 49, 307-345 2. Chordia, T. and L. Shivakumar (2006), 'Earnings and Price Momentum', Journal of Financial Economics 80(3), 627-656 3. Cooper, M., Gutierrez R., and A. Hameed (2004), “Market States and Momentum”, Journal of Finance 59, 1345-1365 4. Daniel, K., Hirschleifer, D. and A. Subrahmanyam (1998), ‘Investor psychology and security market under- and over-reactions’, Journal of Finance 53, 1839–1885. 5. Fama, E. (1998), 'Market Efficiency, Long-term Returns, and Behavioral Finance', Journal of Financial Economics 49(3), 283-306 6. Griffin, J.M., Ji, S., Martin, J.S., (2003), ‘Momentum investing and business cycle risk: evidence from pole to pole’, Journal of Finance 58, 2515–2547. 7. Hong, H., and J. Stein (1999), ‘A unified theory of underreaction, momentum trading, and overreaction in asset markets’, Journal of Finance 54, 2143–2184. 8. Lo, A. and C. MacKinlay, (1990), ‘Data Snooping Biases in Tests of Financial Asset Pricing Models’, Review of Financial Studies 3, 431–468. 9. Jegadeesh, N., & S. Titman (1993), “Returns to buying winners and selling losers: Implications for stock market efficiency”, Journal of Finance 48, 65-93 10. Zhang, F. (2006), “Information Uncertainty and Stock Returns”, Journal of Finance 61, 105-136 Websites 1. Ford Equity Research [Internet]. Available from [Accessed 8 May 2009] Read More