1 Introduction
2 Methodology
3 Results
Study | Sample period | Country | Data set | Target group | (1) | (2) | Key findings |
---|---|---|---|---|---|---|---|
(Odean 1998) | 1987−1993 | USA | Brokerage data | Retail investors | Raw return, excess return | Disposition Effect (1.2) → Negative | Investors keeping both stocks (winners and losers) outperform the disposition effected investors (that keep only the losers) by 2,35% p.a. For winners that are sold, the average excess return over the following year is 3,4% more than it is for losers that are not sold |
(Dhar and Zhu 2006) | 1991 −1996 | USA | Brokerage data | Retail investors | N/A | Disposition Effect (1.2) → N/A Under-diversification (2.1) → N/A | Wealthier individuals and individuals employed in professional financial occupations exhibit a lower disposition effect Trading frequency tends to reduce the disposition effect |
(Kumar and Lim 2008) | 1991−1996 | USA | Brokerage data | Retail investors | Alpha, raw return, Sharpe ratio | Disposition Effect (1.2) → Negative Under-diversification (2.3) → N/A Overtrading (8.1) → N/A Trade Clustering (10.1) → Negative | Investors underperform the common performance benchmarks by 4,32% p.a. Investors with the highest trade clustering (and thus the broadest framing) have a monthly alpha of -0,271% and those investors with the lowest TC (and thus the narrowest framing) have a monthly alpha of -0,407% (delta equals 1,632% p.a.) |
(Bailey et al. 2011) | 1991−1996 | USA | Brokerage data | Retail investors | Alpha, raw return, Sharpe ratio | Disposition Effect (1.2) → Negative Trend chasing (6.1) → N/A Local Bias (4.1) → Negative Lottery Stock Pref. (5.1) → Negative Lottery Stock Pref. (5.3) → Negative Narrow Framing (10.2) → Negative Inattention to Earning News (11.1) → Negative Inattention to Macroeconomic News (11.2) → Negative Overconfidence (9.1) → Negative | Investors on average have a mean monthly alpha of − 0,375%, which translates into risk-adjusted underperformance of 4.50% p.a. Mean monthly return is lower by -0,041% per month for each standard deviation increase in narrow framing (2,12% p.a. lower return for the highest narrow framing group compared to the lowest narrow framing group) Analogous 1,34% p.a. difference between the extreme quintiles of disposition effect |
(Chang et al. 2016) | 2015 | USA | Brokerage data | Retail investors | N/A | Disposition Effect (1.5) → N/A Under-diversification (2.1) → N/A | Larger cognitive dissonance results in higher magnitude of disposition effect (classic and reverse) Investors are more likely to sell again in months in which at least one sale is executed For funds, investors are less likely to sell if it is a gain (reverse disposition effect) |
(Han and Kumar 2013) | 1983−2000 | USA | Brokerage data | Retail investors, stocks | Alpha, raw return | Disposition Effect (1.2) → N/A Disposition Effect (1.3) → Negative Lottery Stock Pref. (5.1) → Negative | The annualized characteristic- and risk-adjusted RTP premium estimates are both about − 7% Speculative retail trading affects stock prices |
(Da Costa et al. 2013) | 1997−2001 | Brazil | Stock market simulation | Retail investors and undergraduate students | N/A | Disposition Effect (1.2) → N/A Disposition Effect (1.1) → N/A Disposition Effect (1.4) → N/A | 69,2% of the subjects have a disposition effect greater 0% More experienced investors are less affected |
(Seru et al. 2010) | 1995−2003 | Finland | Brokerage data | Retail investors | Raw return | Disposition Effect (1.6) → N/A | Evidence of two types of learning: some investors become better at trading with experience, while others stop trading after realizing that their ability to trade is poor Investors whose performance are one standard deviation worse than the mean are about 15% less likely to continue trading |
(Meyer et al. 2012a) | 2000−2007 | Germany | Brokerage data | Retail investors | Alpha, Sharpe ratio modified Dietz method | Disposition Effect (1.2) → N/A Under-diversification (2.1) → N/A Under-diversification (2.2) → N/A Overtrading (8.1) → N/A | Investors learn (improve their performance) by trading: 100 additional active trades are associated with an increase in portfolio performance of 0,15% per month One additional month of active trading is associated with an increase in monthly portfolio returns by 0,02% (0,24% p.a.) |
(Weber et al. 2014) | 1999− 2011 | Europe | Brokerage data | Retail investors | Alpha, raw return, Sharpe ratio | Disposition Effect (1.6) → Positive Trend chasing (6.1) → Positive Under-diversification (2.2) → Negative Under-diversification (2.3) → Negative Home Bias (3.2) → Positive Local Bias (4.1) → Positive Lottery Stock Pref. (5.2) → Negative Overtrading (8.1) → Negative Narrow Framing (10.1) → Positive Leading Turnover Share (7.1) → Negative | The mean investor has a 4,2% lower annual portfolio returns than a fully diversified investor Lottery stock preference costs an investor 3,31% p.a. while low trade clustering (narrow framing) improves performance by 2,13% p.a For funds, trend chasing improves performance by 1,09% p.a. |
(Goetzmann and Kumar 2008) | 1991−1996 | USA | Brokerage data | Retail investors | Monthly excess return, Sharpe ratio | Disposition Effect (1.2) → N/A Trend chasing (6.1) → N/A Under-diversification (2.1) → Unclear Under-diversification (2.2) → Unclear Under-diversification (2.3) → Unclear Local Bias (4.2) → Unclear Overtrading (8.1) → Unclear Overconfidence (9.1) → N/A | The lowest diversified decile has a lower alpha of 0,12% per month (1,44% p.a.) than the highest diversified decile Under-diversification is costly to most investors, but a small subset of investors under-diversified because of superior information Investors who trade excessively, tilt their portfolios toward local stocks, and are sensitive to past price trends exhibit greater under-diversification as well as earning lower returns Most surprisingly, high-turnover, under-diversified portfolios perform better than high-turnover, better-diversified portfolios |
(Calvet et al. 2006) | 1999–2002 | Sweden | Includes all trading data in Sweden | All individual investors | Alpha, Return loss, Sharpe ratio loss | Under-diversification (2.4) → Negative | A subset of the sample loses up to 10% p.a. due under-diversification and 4,3% p.a. for being too risk averse Sophisticated investors invest more efficiently and aggressively (higher risk appetite) Most households outperform the domestic benchmark via international diversification Diversification is supported by funds (76% of investors hold funds as well as stocks) |
(Grinblatt et al. 2011) | 1995–2002 | Finland | Includes all trading data in Finland | All individual investors | Sharpe ratio | Under-diversification (2.1) → N/A | Higher IQ leads to a higher Sharpe ratio (mainly because of a lower volatility), more stocks held and a higher probability for a fund to be held (leading to a more diversified portfolio) |
(Gaudecker 2015) | 2005–2006 | Netherlands | Dutch household survey | All individual investors | Sharpe ratio | Under-diversification (2.4) → Negative | The market coefficient rises continuously with the return loss meaning that riskier portfolios have lower return The average return loss of an investor is 0,552% p.a. (risk-adjusted loss compared to the market) Largest losses resulting from under-diversification are incurred by those who neither turn to external help with their investments nor have good skills in basic financial-numerical operations and concepts |
(Graham et al. 2009) | 1999–2002 | USA | UBS/Gallup Investor Survey | Retail investors | N/A | Home Bias (3.3) → N/A | When an investor feels competent about understanding the benefits and risks involved in investing in foreign assets, he is more willing to invest in foreign securities. In contrast, when an investor feels less competent, he is more likely to avoid foreign assets High competence (assessed by the investors themselves) correlates with higher trading frequency |
(Von Nitzsch, R. and Stotz, O., 2005) | 1979–2005 | Multiple | Previous studies | Indices | Sharpe ratio, return for a given volatility | Home Bias (3.1) → Unclear | A higher international diversification leads to a higher return For investors in countries with a high market capitalization and a developed capital market, such as the US, the UK, and Japan, the loss in returns can be expected to be lower than 1% p.a.; for Germany, France, Italy, and Canada, the costs from a home bias are even likely to be higher than 1% p.a. For the USA, home bias even indicated a positive impact on performance |
(Coeurdacier and Rey 2013) | 2008 | Multiple | Publicly available data sets | Indices | N/A | Home Bias (3.4) → N/A | The average home bias is 0,63 across multiple countries being lower in Europe (about 0,5) and higher in emerging markets (0,9) Home bias has become less and less over time |
(Coval and Moskowitz 2001) | 1975–1994 | USA | US mutual funds data | Mutual fund managers | Raw returns, Alpha | Local Bias (4.2) → Positive | Local investments of fund managers achieve an excess return of 2,67% p.a. compared to non-local stocks—although the Sharpe ratio is not different (meaning local investments are also riskier) |
(Ivković and Weisbenner 2005) | 1991−1996 | USA | Brokerage data | Retail investors, stocks | Alpha, raw return, excess return | Under-diversification (2.1) → N/A Local Bias (4.1) → N/A Local Bias (4.2) → Positive | The average household generates an additional return of 3,7% per year from its local holdings relative to its non-local holdings, suggesting that local investors are able to exploit local knowledge The excess return to investing locally is even larger among stocks not in the S&P500 index (firms where informational asymmetries between local and non-local investors may be largest), while there is no excess return earned by households that invest in local S&P500 stocks Distance to own portfolio is on average 308 miles lower than to the market portfolio |
(Seasholes and Zhu 2010) | 1991−1996 | USA | Brokerage data | Retail investors | Alpha, excess return | Local Bias (4.2) → Positive | Share of local stocks in investor’s portfolio is 30% (overweight), while only 12% of the market is head-quartered within the same radius Investors local portfolios outperform the market by 0,8% p.a. |
(Kumar 2009) | 1991−1996 | USA | Brokerage data | Retail investors, stocks | Alpha, raw return | Lottery Stock Pref. (5.1) → Negative Lottery Stock Pref. (5.2) → Negative Lottery Stock Pref. (5.4) → N/A | Lottery stocks have a monthly alpha -0,52% p.m. (-6,23% p.a.) The risk-adjusted performance difference between lottery and non-lottery stocks is -7,1% p.a. Lottery stocks have roughly double the standard deviation compared to non-lottery stocks Lottery stocks make up 3,74% of an average retail portfolio and only 0,76% of an average institutional portfolio As a group, lottery-type stocks represent 1.25% of the total stock market capitalization, but in terms of their total number, they represent about 13% of the market |
(Bali et al. 2011) | 1926–2005 | USA | Stock exchange data | Stocks | Alpha | Lottery Stock Pref. (5.1) → Negative | Lottery stocks are determine based on the maximum daily return over the past one month (MAX) The alpha of the lowest decile MAX-portfolio is 1,18% p.m. (14,16% p.a) higher than of the highest decile MAX-portfolio |
(Bali et al. 2017) | 1963–2012 | USA | Stock exchange data | Stocks | Alpha | Lottery Stock Pref. (5.1) → Negative | The alpha of the lowest decile MAX-portfolio is 1,4% p.m. (16,8% p.a) higher than of the highest decile MAX-portfolio.– Lower MAX means lower lottery characteristics |
(Bali et al. 2021) | 1963–2017 | USA | US stock exchange data | Stocks | Alpha | Lottery Stock Pref. (5.1) → Negative | The highest decile of lottery characteristics portfolio has a -0,71% p.m. (−8,52% p.a.) lower monthly alpha than the lowest decile Consistent with previous evidence that lottery demand is attributable to individual, not institutional, investors, the beta anomaly is concentrated among stocks that have low institutional ownership |
(Bergsma and Tayal 2019) | 1988–2015 | USA | Stock exchange data | Stocks | Alpha | Lottery Stock Pref. (5.1) → Negative | The highest lottery stock quintile of high RSI (relative short interest) stocks has a four-factor alpha of -1,61% p.m. (-19,32% p.a.) Weaker arbitrage in high RSI lottery stocks leads to overpricing of the stocks |
(Odean 1999) | 1987−1993 | USA | Brokerage data | Retail investors | Alpha | Overtrading (8.1) → Negative | The purchases of investors underperform their sales by an average return of 3,31% p.a. Returns of sold stocks are statistically significantly higher than bought stocks |
(Barber und Odean 2001) | 1991−1996 | USA | Brokerage data | Retail investors | Alpha | Overtrading (8.1) → Negative | Men have a monthly portfolio turnover of 6,41% while women have 4,4% (Difference of 2,01%) Both men and women achieve a lower return by trading The stocks they sell earn reliably greater returns than those they buy (men: 2,4% p.a.; women: 2,04% p.a.) |
(Barber and Odean 2000) | 1991−1996 | USA | Brokerage data | Retail investors | Alpha, raw return | Overtrading (8.1) → Negative | The average household underperforms the market by 1,5% p.a. High trading turnover costs households 6,8% p.a. relative to the returns earned by low turnover households |
(Fischbacher et al. 2017) | 2015 | Germany | Stock market simulation | Students | N/A | Disposition Effect (1.2) → N/A | In the experiment, automatic selling devices helped investors increase the proportion of losers realized but did not affect the proportion of winners realized still leading to a reduction of the disposition effect |
(Dhar und Kumar 2001) | 1991–1996 | USA | Brokerage data | Retail investors | Raw return | Disposition Effect (1.2) → Unclear Disposition Effect (1.3) → Unclear Under-diversification (2.1) → N/A Trend chasing (6.1)→ Unclear | For contrarian investor group, the average 21-day return for the stocks they sell is significantly higher (8,84%) than the average 21-day return for stocks they hold (1,78%). In contrast, the average 21-day return for the stocks the momentum investors sell is significantly lower (− 2,98%) than the average return for stocks they hold (1,40%) |
(Sirri and Tufano 1998) | 1971–1990 | USA | Publicly available data sets | Flow of funds | Alpha, excess return | Trend chasing (6.1) → N/A | High performance appears to be most salient for funds that exert higher marketing effort, as measured by higher fees. Flows are directly related to the size of the fund’s complex as well as the current media attention received by the fund, which lower consumers’ search costs Consumers of equity funds disproportionately flock to high-performing funds while failing to flee lower-performing funds at the same rate |
(Ivković et al. 2008) | 1991–1996 | USA | Brokerage data | Retail investors | Alpha, Sharpe ratio | Under-diversification (2.1) → Positive Under-diversification (2.2) → Positive Local Bias (4.2) → Positive Overtrading (8.1) → N/A | Concentrated portfolios of individual investors outperform diversified portfolios which implies a successful exploitation of information asymmetries Concentrated households have a 0,16% p.m. (1,92% p.a.) higher alpha than diversified portfolios Portfolios with fewer stocks have a higher return than those with more stocks but they are associated with higher risks and a lower Sharpe ratio Results are not driven by specialization in a particular industry, inside information, broad market timing, repeated trades in a particular stock, or regional differences across investors. Rather, the results seem to reflect that wealthy households who concentrate their holdings in a few stocks tend to have the ability to identify superior stock picks |
(Meyer et al. 2012b) | 2005–2010 | Germany | Brokerage data | Retail investors | Alpha, raw return | Home Bias (3.2) → N/A | Based on gross returns, individual investors have an average skill of approximately -7.5% p.a. 89% of individual investors exhibit negative skill (α ≤ 0) when measured on a gross basis and 91% when considering returns net of costs and expenses |
(Faruqee et al. 2004) | 2004 | Multiple | Survey of international portfolio holdings | Indices | Raw return | Home bias (3.5) → Negative | International portfolio holdings are determined by market size, transaction costs, and information costs. The estimation results also support explanatory return-chasing behavior and portfolio diversification as implied by the international CAPM. These results suggest that international investing behavior is determined by multiple factors, which helps explain why single-factor models are inadequate in solving the home bias puzzle Financial market size and information asymmetry are major determinants of international portfolio choice and home bias |
3.1 Publication year and sample data
3.2 Duration of examinations and target groups
3.3 Bias-induced investment behaviors
Bias factor | Description | Proxy for | (1) | (2) | Effect |
---|---|---|---|---|---|
Disposition effect | Tendency to sell winners too early and hold losers too long | Loss aversion | 13 | 6 | Negative |
Under-diversification | Tendency to hold few or concentrated positions | Overconfidence | 12 | 4 | Negative |
Home bias | Tendency to select stocks with headquarters in the investor’s home country | Familiarity bias | 6 | 5 | Positive and negative |
Local bias | Tendency to select stocks with headquarters close to the investor’s geographical location | Familiarity bias | 6 | 2 | Positive and negative |
Lottery stock preference | Tendency to select stocks with lottery-like features (low price, volatile, and high recent return) | Gambling preference | 8 | 4 | Negative |
Trend chasing | Tendency to chase assets with high recent returns | Extrapolation bias | 4 | 1 | Positive and negative |
Leading turnover share | Tendency to systematically trade before other investors | Informed trading | 1 | 1 | Negative |
Overtrading | Tendency to trade frequently | Overconfidence | 8 | 1 | Positive and negative |
Overconfidence | Tendency to trade frequently but unsuccessfully | Overestimate one’s knowledge or skill | 2 | 1 | Negative |
Trade clustering | Tendency to select investments individually instead of considering the broad impact on the portfolio | Narrow framing | 3 | 2 | Positive and negative |
Inattention to news | Tendency to (not) trade a particular individual stock around a news event | Uninformed trading | 1 | 2 | Negative |
# | Operationalization of behavioral bias factors | (1) | (2) |
---|---|---|---|
1.1 | \({\text{D}}{{\text{isposition}}\, {\text{Effect}}}_{it} =\frac{{{\text{Realized}}\, {\text{Gains}}}-{\text{Realized}}\, {\text{Losses}}} {{{\text{Realized}}\, {\text{Gains}}+{\text{Realized}} \,{\text{Losses}}}}\) | −1 – 1 | 1 |
1.2 | \({\text{D}}{{\text{isposition}}\, {\text{Effect}}}_{it} =\frac{{{\text{Realized}} \,{\text{Gains}}}} {{{\text{Realized}} \,{\text{Gains}}+{\text{Unrealized}} \,{\text{Gains}}}}-\frac{{{\text{Realized}} \,{\text{Losses}}}} {{{\text{Realized}} \,{\text{Losses}}+{\text{Unrealized}} \,{\text{Losses}}}}\) | −1 – 1 | 10 |
1.3 | \({\text{D}}{{\text{isposition}}\, {\text{Effect}}}_{it} =\frac{\frac{{\text{Realized}}\, {\text{Gains}}}{{\text{Realized}}\, {\text{Gains}}+{\text{Unrealized}}\, {\text{Gains}}}}{\frac{{\text{Realized}} \, {\text{Losses}}}{{\text{Realized}} \,{\text{Losses}}+{\text{Unrealized}} \,{\text{Losses}}}}\) | 0 – + \(\infty\) | 2 |
1.4 | \({\text{D}}{{\text{isposition}} {\text{Effect}}}_{it} =\frac{{{\text{Realized}} \,{\text{Gains}}}} {{{\text{Realized}} \,{\text{Losses}}}} - \frac{{{\text{Paper}}\, {\text{Gains}}}}{{{\text{Paper}} \,{\text{Losses}}}}\) | \(-\infty\) – + \(\infty\) | 1 |
1.5 | \({{\text{Sale}}}_{ijt} = {b}_{0} + {b}_{1}{{\text{Gain}}}_{ijt} + {\epsilon }_{ijt}\) | \(-\infty\) – + \(\infty\) | 1 |
1.6 | Cox Regression (Cox 1972) | \(-\infty\) – + \(\infty\) | 2 |
2.1 | Number of Stocks = N | 1 – + \(\infty\) | 8 |
2.2 | \({\text{Herfindahl}} \,{\text{Index}} ={\sum }_{i=1}^{N}{{\text{weight}}}_{i}^{2}\) | 1 –\(\to 0\) | 4 |
2.3 | \({\text{Normalized}} \,{\text{Portfolio}}\, {\text{Variance}} =\frac{{\sigma }_{p}^{2}}{{\overline{\sigma }}^{2}}\) | 0 – 1 | 3 |
2.4 | \({\text{Return}} \,{\text{Loss}} ={\mu }_{b} * {\omega }_{h} * {\beta }_{h} *\frac{{{\text{Sharpe}}\, {\text{Ratio}}}_{b}-{{\text{Sharpe}} \,{\text{Ratio}}}_{h}}{{{\text{Sharpe}} \,{\text{Ratio}}}_{h}}\) | \(-\infty\) – + \(\infty\) | 2 |
3.1 | \({\text{Home}}\,{{\text{Bias}}}_{i} =\frac{{{\text{Share}} \,{\text{of}}\, {\text{Domestic}} \,{\text{Equities}}}}{{{{\text{Share}} \,{\text{of}}\, {\text{Domestic}} \,{\text{Equities}}}_{{\text{Benchmark}}}}}\) | 0 – 1 | 1 |
3.2 | \({\text{Home}}\, {{\text{Bias}}}_{i} = {\text{Share}} \,{\text{of}} \,{\text{Domestic}} \,{\text{Equities}}\) | 0 – 1 | 2 |
3.3 | \({\text{Home}} \,{{\text{Bias}}}_{i} = {\text{Share}} \,{\text{of}}\, {\text{Foreign}}\, {\text{Equities}}\) | 0 – 1 | 1 |
3.4 | \({\text{Home}}\, {{\text{Bias}}}_{i} =1- \frac{{{\text{Share}} \,{\text{of}}\, {\text{Foreign}}\, {\text{Equities}}}}{{{{\text{Share}} \,{\text{of}}\, {\text{Foreign}} \,{\text{Equities}}}_{{\text{Benchmark}}}}}\) | 0 – 1 | 1 |
3.5 | \({\text{Home}}\, {{\text{Bias}}}_{i} = {\text{Share}} \,{\text{of}} \,{\text{Domestic}} \,{\text{Equities}}-{{\text{Share}} \,\mathrm{of \,} {\text{Equities}}}_{{\text{Benchmark}}}\) | −1 – 1 | 1 |
4.1 | \({\text{A}}{{\text{verage}} \,{\text{Distance}}}_{it} ={\sum }_{i=1}^{N}\left({{\text{distance}}}_{ij}\cdot {{\text{weight}}}_{ijt}\right)\) | 0 – + \(\infty\) | 3 |
4.2 | \({\text{L}}{{\text{ocal}}\, {\text{Bias}}}_{i} = {\text{Share}}\, {\text{of}} \,{\text{Local}} \,{\text{Equities}}\) | 0 – 1 | 5 |
5.1 | \({\text{L}}{\mathrm{ottery Preference}}_{it} = \mathrm{Share \,of \,Lottery\, Stocks}\) | 0 – 1 | 7 |
5.2 | \({\text{L}}{\mathrm{ottery \,Preference}}_{it}=\frac{{\text{Share}} \,{\text{of}}\, {\text{Lottery}}\, {\text{Stocks}} -{\mathrm{Share\, of\, Lottery\, Stocks}}_{{\text{Benchmark}}}}{{\mathrm{Share \,of\, Lottery\, Stocks}}_{Benchmark}}\) | 0 – 1 | 2 |
5.3 | \({\text{L}}{\mathrm{ottery\, Preference}}_{it}=\frac{\mathrm{Share \,of\, Lottery\, Stocks} }{{\mathrm{Share\, of\, Lottery\, Stocks}}_{{\text{Benchmark}}}}\) | 0 – 1 | 1 |
5.4 | \({\mathrm{Lottery\,Preference}}_{it}=\mathrm{Share\, of\,Lottery\, Stocks\, Purchases}\) | 0 – 1 | 1 |
6.1 | \(\mathrm{Average trend before all trades}\left(k\right)={\sum }_{j=1}^{{N}_{i}}r\left(i, j, k\right)\) | \(-\infty\) – + \(\infty\) | 5 |
7.1 | \({\text{L}}{\mathrm{eading\, Turnover\, Share}}_{{\text{it}}}=\frac{{{\text{Leading}}\, {\text{Turnover}}}}{{{\text{Total}}\, {\text{Turnover}}}}\) | 0 – 1 | 1 |
8.1 | \({\text{Average}} {\text{Portfolio}} {\text{Turnover}}=\frac{1}{2}\cdot {\text{min}}\left(\frac{{\text{Purchase}} {{\text{Volume}}}_{t-1}}{{\text{Portfolio}} {{\text{Volume}}}_{t}}\right)+\frac{1}{2}\cdot {\text{min}}\left(\frac{Sale\, Volum{e}_{t-1}}{{\text{Portfolio}}\, {{\text{Volume}}}_{t}}\right)\) | 0 – + \(\infty\) | 7 |
9.1 | \({\text{Overconfidence}} \,{\text{Dummy}}\, {\text{Variable}}\, \mathrm{equal \,to \,one\, for\, investors\, in \,the\, highest\, portfolio}\) \(\mathrm{turnover\, quintile \,and \,lowest \,performance\, quintile\, for\, their\, individual\, common\, stock\, trading\, and\, zero\, otherwise}\) | 0 –1 | 2 |
10.1 | \({\text{T}}{{\text{rade}} \,{\text{Clustering}}}_{it} =1-\frac{{{\text{Number}} \,{\text{of}} \,{\text{trading}}\, {\text{days}}}}{{{\text{Number}}\, {\text{of}}\, {\text{trades}}}}\) | 0 – 1 | 2 |
10.2 | \({\text{T}}{\mathrm{rade\, Clustering}}_{{\text{it}}} =1-\frac{{{\text{Number}} \,{\text{of}} \,{\text{trades}}}}{{{\text{Number}} \,{\text{of}}\, {\text{trading}}\, {\text{days}}}}\) | −\(\infty -0\) | 1 |
11.1 | \({\text{Inattention}} \,{\text{to}}\, {\text{Earning}}\, {\text{News}} = 1 {-} \left( {{\text{number}}\, {\text{of}}\, {\text{investor}}\, {\text{trades}}\, {\text{around}}\, {\text{the}}\, {\text{event}}} \right)/\left( {{\text{total}}\, {\text{number}} \,{\text{of}}\, {\text{investor}} \,{\text{trades}}} \right)\) | 0 – 1 | 1 |
11.2 | \({\text{Inattention}}\, {\text{to}}\, {\text{Macroeconomic}} \,{\text{News}} = 1 {-} \left( {{\text{number}} \,{\text{of}}\, {\text{investor}} \,{\text{trades}}\, {\text{around}} \,{\text{the}} \,{\text{event}}}\, \right)/\left( {{\text{total}}\, {\text{number}} \,{\text{of}} \,{\text{investor}} \,{\text{trades}}} \right)\) | 0 – 1 | 1 |
3.3.1 Operationalized behavioral biases
3.3.1.1 Biases related to portfolio composition
3.3.1.2 Biases related to the decision-making process
3.3.1.3 Biases related to stock picking
3.3.2 Operational differences to measure behavioral biases
3.3.2.1 Different data requirements
3.3.2.2 Different mathematical computations
3.3.2.3 Different theoretical ranges
3.3.3 Performance measures and effects of behavioral biases
4 Discussion
4.1 Behavioral bias factors and their measurements
4.2 Comparability of bias factors
5 Conclusion
Behavioral bias factor | Formula(s) |
---|---|
Disposition effect (DE) | 1.2 |
Under-diversification (UD) | 2.1, 2.2, 2.3 |
Home bias (HB) | 3.2 |
Local bias (LB) | 4.2 |
Lottery stock preference (LSP) | 5.1 |
Trend chasing (TCH) | 6.1 |
Overtrading (OT) | 8.1 |
Trade clustering (TCL) | 10.1 |