how individual investor are affected individual investor and force of emotion.pdf

Behavioral Finance Dr. Evangelos Vagenas-Nanos Room 359A, Main Building, West Quadrangle Lectures 7: How Individual Investors are Affected by Sentiments; Individual Investors and the Force of Emotion; Behavioral Explanations for Anomalies • Individual investors and the force of emotion (AD, Ch. 10) –How individual investors are affected by sentiments (10.2 and KLVVM) –Kaplanski, Levy, Veld, and Veld-Merkoulova (2014): “Do happy people make optimistic investors” forthcoming in the Journal of Financial and Quantitative Analysis • Emotion in Aggregate Level –Siganos, Vagenas-Nanos, Verwijmeren (2014): “Facebook’s daily sentiment and international stock markets” R Pecjak (1970); Croft and Walker (2001); Larsen and Kasimatis (1990) –Does mood explain the Monday Anomaly? Paper 2: Abu Bakar, Vagenas-Nanos, Verwijmeren (2014): “Does mood explain the Monday effect?” Motivation 00.050.10.150.20.250.3MondayTuesdayWednesdayThursdayFriday-0.3-0.25-0.2-0.15-0.1-0.0500.05MondayTuesdayWednesdayThursdayFridayReturns in days with the highest 10 percent mood Returns in days with the lowest 10 percent mood Main Findings (1) (2) (3) Mondayit -0.091*** -0.040 (0.000) (0.174) Moodit 2.523*** (0.000) Moodit *Mondayit 4.295*** (0.000) Mood(F-M)it 2.248 (0.267) _cons 0.062 0.090** -0.026 (0.173) (0.049) (0.870) N 21,902 21,902 4,113 F statistic 1.400 3.825 0.876 • Under efficient markets: –The market reaction should depict future potential synergies discounted by an appropriate discount factor • Emotions have an impact on judgment Wright and Bower (1992); Bless et al. (1996); Johnson and Tversky (1983) and Loewenstein et al. (2001) • Investors in a good mood may overestimate synergies and/or underestimate risk Paper 3: Siganos, Vagenas-Nanos, Danbolt (2014): Does Mood Impact on Acquirers’ Announcement Abnormal Returns? Stock Market Reaction =𝐹𝑣𝑢𝑣𝑠𝑒 𝑐𝑎𝑡ℎ 𝐹𝑙𝑝𝑤𝑡 + 𝑆𝑦𝑜𝑒𝑠𝑔𝑖𝑒𝑡 𝐷𝑖𝑡𝑐𝑝𝑣𝑜𝑢 𝐹𝑎𝑐𝑢𝑝𝑠Stock Market Reaction =𝐹𝑣𝑢𝑣𝑠𝑒 𝑐𝑎𝑡ℎ 𝐹𝑙𝑝𝑤𝑡 + 𝑆𝑦𝑜𝑒𝑠𝑔𝑖𝑒𝑡 𝐷𝑖𝑡𝑐𝑝𝑣𝑜𝑢 𝐹𝑎𝑐𝑢𝑝𝑠• We empirically investigate the impact of mood on M&As announcement abnormal returns • We hypothesize a positive relationship between the level of mood and bidder abnormal returns Hypotheses Main Findings All High GNH 0.019*** (0.000) N 2431 2 GNH 0.018*** (0.000) 3 GNH 0.014*** (0.000) Low GNH 0.012*** (0.000) N 2426 High-Low 0.007*** (0.008) All GNH 0.110*** (0.004) BTMV 0.001** (0.048) Domestic -0.004 (0.253) Stock 0.016** (0.018) Diversified -0.006*** (0.001) Public -0.028*** (0.000) Constant 0.022*** (0.000) N 8896 adj. R-sq 0.014 • Mood has a positive impact on bidder’s abnormal returns • More pronounced for public acquisitions, low strategic blockholders, high relative size deals Main Findings • The main approach in the literature is examine the level of sentiment on stock market characteristics • An average level of sentiment on a specific day may hide variation is sentiment • Aggregate level of sentiment on a specific day could be zero but that may mean that half of the population may be happy and the other half may be unhappy Paper 4: Siganos, Vagenas-Nanos, Verwijmeren (2014): “Divergence of Sentiment” • We investigate the impact of the distance (divergence) between happy and unhappy individuals on stock market trading and volatility Divergence of Sentiment 0 0 +1 -1 -5 +5 • Theoretical disagreement models predict that higher disagreement is associated with more trading –Karpoff (1986), Varian (1989), Harris and Raviv (1993) and Banerjee and Kremer (2010) • Disagreement also leads to higher absolute price changes and therefore to higher volatility • Preliminary findings support the above hypotheses. Disagreement Models • In addition to the anomalies mentioned in KLVVM, AD (10.2) mention the “Clock changes” anomaly: –Kamstra, Kramer, and Levi (American Economic Review, 2002): Stock markets fall when traders’ sleep patterns are disrupted due to clock changes due to daylight savings time Mood (10.2) • AD: Overall there is a lot of evidence that sentiments (mood) affect investment behaviour • However: 1) Results are not conclusive (e.g. weather effect) 2) Results (until 2010) are based on macro studies 3) It is not clear that there is a simple way to characterize relation between mood and risk attitude. • When someone is in a poor mood does he take more risks or fewer? –Answer probably depends on context and individual’s personality Mood and Risk Attitude 1.Some research suggests happier people are more optimistic and assign higher probabilities to positive events. 2.But at the same time, other decision-making research indicates that even though people may be more optimistic about their likelihood of winning a gamble when they are happy, same people are much less willing to actually take the gamble. –In other words, they are more risk averse when they are happy • The evidence from KLVVM suggests that (1) is more likely than (2) M。