Noise traders Black (1986) first introduced noise traders as vital for the existence of a liquid market and causes inefficiency in the market. However he disagree that the idea of efficient market hypothesis (EMH) that were introduced by Fama (1986) as he believes that noise traders could cause the inefficiency of the market. Shefrin and Statman (1994) define noise traders as uninformed traders that do not process information rationally. However Ramiah & Davidson (2007) has a more extensive definition on noise traders where noise traders happen by chance and always base on their expectations on the information therefore there is possibilities that they make mistakes and they are mainly small traders.
Topic lead paper (General)
There has been a lot of interest in area of research on noise traders in the financial markets by Black (1986) , De Long, Scheifer, Summers and Waldman (DSSW) (1990) risk of noise traders in the financial market, Odean (1998), Shefrin and Statman (1994) the BAPM, Ramiah and Davidson (2007) on general noise trader model that can be applied generally using the IANM, Ng and Wu (2006) on stock preferences of investors on share-A or share-B, Zhang & Yang (2009) on stock price based on the noise trading theory, Konte (2010) valuation on mispriced assets If reflective on noise traders or limited arbitrage, and recent research Bloomfield, O'hara, & Saar (2009), where find that noise traders have positive and negative effects on the market.
Behavioral Literature review (General)
Behavioral literature focused
Kahneman and Tversky (1974) introduce anchoring effect as people's perceptions rely too heavily on one piece of information or recent experience when making decisions and uncertainty. The author indicates that the subjects' decisions to estimate mean percentage of African countries in United Nation are influenced by numbers generated by the wheel of fortune. Kahneman and Tversky (1974) and Epley and Gilovich (2006) define phenomenon anchoring as people make estimation starting from an initial value that is adjusted to yield the final answer which is biased towards the initial values.
The decision making process plays an important role in the financial industry, and on most recent years, there has been a lot of studies on anchoring bias in the financial markets. Westerhoff (2003) traders in the foreign exchange market. Chang and Ren (2008) dual listed companies in Hong Kong and China markets where A-shares valued based on H-shares are underpriced and most recent Ling, Hilary and Wei (2010) find investors estimate the firm future profitability. Kaustia M, Alho E, Puttonen V (2008), finds that financial market professional do have a small and significant anchoring effect
Anchoring effect can cause wrong estimation from initial value could lead to systematic and predictable errors. Epley and Gilovich (2001) state that wrong adjustment could lead a bias towards initial anchor value. Ling, Hilary and Wei (2010) find that low FEPS stocks are overpriced. Amirl and Ganzach (1998) and Marsden, Veeraraghavan, Ye (2008) stated that heuristic behavioral bias which includes anchoring and adjustment could influence forecast and leniency thus leads to overreaction to positive adjustment and under reaction to negative adjustment. They also find that the time horizon do factor the gap of biasness, whereby the longer the time frame the larger the prediction bias.
Gap Rational
There has been a lot of behavioral finance and noise trader research made on the financial markets and its investors however there is none on social responsible investment (SRI). According to Social Investment Forum (1999), SRI is defined as investors that made investment decisions based on personal values and societal concerns. The recent BP oil spill incident has shown an upshot on social responsible investing reported by Laise E. (2010).
Williams (2007) states that SRI drives investors into more social returns rather than profit return thus influence their investment choices. Hamilton et al., 1993 find that SRI performance does not imply lower profits to other stocks. Renneboog L., Horst J.T, Zhang C., (2008) find a positive abnormal returns on SRI firms that released public information of good governance and socially responsible. The current existing literature has been focusing the performance between SRI and stocks market and SRI firm and investor general behavior (Rosen et al. 1991). There is no research available on effects of any anchoring bias of SRI investors and noise trading in SRI.
Methodology
Ramiah and Davidson 2007 tested existence of Noise Traders using IANM, measured base on the "Mums and Dads" Index (MDI) because they believe not likely that the information traders would likely always automatically correct the mistakes.
We will attempt to use this model to find out noise traders existence in the SRI. There has been many indexes creation based on different stock selection, widely known are FTSE KLD 400 Social Index, the FTSE4Good US Index, the US component Dow Jones Sustainability Index (DJSI), and the KLD Select Social Index.
We will focus on the FTSE4Good Australia 30 Index and All Ordinary Index (AOI) as benchmark for traditional market proxy to test on the IANM model of Ramiah and Davidson 2007. According to FTSE 2010, FTSE4Good Australia 30 Index selects stocks based on environment and social causes and does not include stocks involved in tobacco, firearms, and nuclear power.
We will use based on Shefrin and Statman (1994), CAPM model as it does not allow noise traders to generate biased, therefore we use AOI as traditional market proxy.
Equation 1
Where is the stock i's return at time t
is the risk free return for the SRI mutual fund at time t
is the return on the market at time t (AOI)
is the error term
is the intercept of the regression equation
is the CAPM beta
BAPM Equation will be used to capture noise traders in FTSE4Good Australia 30 Index will be as follows:
Equation 2
Where
is the return on the behavioral market portfolio at time t (FTSE4Good Australia 30 Index).
is the efficient beta or behavioral beta and capture some noise effect.
In Ramiah and Davidson 2007, states that to extract information of out of element BE is the best way to measure noise trader.
The BE defined by the relation between CAPM and BAPM
Equation 3
The following formula could indicate if this noise traders reaction could cause an overreaction or under reaction in the market.
= ï¡ + ï¢IEit + ï¥it Equation 4
Where
is the change in the behavioral error for stock i on day t.
IE is an information event, i.e. the arrival of news. Dummy variable indicate value of 1 on days information is released to the market, and = 0 when no information is released to the market. The market then does not differentiate between 'good' or 'bad' news.
ï¡ is the mean change in the behavioral error attributable to noise traders
ï¢ is the proportion of the mean change in behavioral error attributable to information traders. Ramiah and Davidson (2007) assume shows that ï¢ ï‚¹ 0.
ï¥it is the error term
To test the existence of anchoring bias, we will be using model of non parametric test based on interview and empirical test. For the non parametric test, we will interview SRI investors
First, we described two types of stocks, stock A and stock B with identical pricing, business, characteristics, size, performance, market power, SCI listed firm difference between both stocks is the news (one good and another bad on social responsibility) and returns (the good with low return, the bad news with higher return) we show a current and forecast returns of the stocks, and we will ask which stock would likely desirable to be invested. Here we would like to test if investors is anchor towards their opinion when decision making on investing.
The purpose of this paper is to find if among SRI investors there's existence of noise traders portray anchoring bias in the FTSE4Good Australia 30 Index. As there has no actual data used to conduct this outcome, we cannot confidently conclude evidence of noise traders existence in FTSE4Good Australia 30 Index with under influence of anchoring bias. Based on a general assumption and hypothesis we able to draw a drafted outcome of the conclusion of this paper as the following: FTSE4Good Australia 30 Index do seem to portray noise trader behaviour under influenced of anchoring bias in the FTSE4Good Australia 30 Index because investors likely monitoring more on the firms actions, less desirable news likely influence and overreact the investors behaviour and vice versa.
Conclusion
Financial behavior in SRI has not been widely researched despite investors, government and NGO's has been rapidly increasing in demand these recent years. It is important that financial advisors and planners to understand SRI investors decision making behavior so that they able to meet SRI investors goals and needs.
We believe that SRI investors do portray anchoring and adjustment based on released information, and hence be potential noise traders that react that caused overreaction and under reaction that could explain the positive abnormal returns on SRI firms. There will be possibility that SRI may not react to information or react to information, based on their value and interpretation which is relevant to anchoring bias. There are many SRI strategy methods we will focus on negative screening method. For example, investors may exclude investing tobacco or military hardware stocks.
