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A relevant question is whether an exclusive set of factors, or empirical principles, can generate the observed behaviour and cognitive biases of individuals who take financial decisions, such as which stock to sell, how to spend money, and which loan to select (Frydman and Camerer, 2016). According to a research, there are many examples of inferior decision-making, especially in retail equity investors: cost- insensitivity when selecting mutual funds (Elton, Gruber, and Busse, 2004), slow re-balancing (Brunnermeier and Nagel, 2008); paying for minimally-customised advice (Foerster, Linnainmaa, Melzer, and Previtero, 2017); selling winners and keeping losers (Odean, 1998), (Barberis and Xiong, 2009); over-trading (Barber and Odean, 2000); over-weighting domestic stocks (Ahearne, Griever, and Warnock, 2004); false beliefs in their investment opinion (Egan, Merkle, and Weber, 2014); and inaccurate valuations when faced with product complexity (J.R. Brown, Kapteyn, Luttmer, and Mitchell, 2013). Research done previously also suggested that discovering good predictors of this kind of sub-optimal behaviour is an important objective and aim; improvements to household financial decisions can have a positive impact not just on those directly affected but on society as a whole (Bhamra and Uppal, 2019).
Individuals, according to a reasonable viewpoint, are multifaceted, behaving in response to non-trivial tasks in ways that are shaped by personality, beliefs, preferences, attitudes, domain knowledge, and task-specific tactics. Researchers have acknowledged this implicitly or explicitly by attempting to explain investor behaviour, cognitive biases, and possible outcomes in an investment setting using diverse ideas from the disciplines of finance, economics, and psychology. Risk aversion, financial literacy, time preference, economic beliefs, cognitive ability, and personality traits are examples of these concepts.
For this research work, performance (using Sharpe ratio), biases (overconfidence, disposition bias, unrewarded risk), and behaviour are all covered by our nine dependent variables (activity relating to portfolio self-management and stock trading, such as turnover). Each variable, for the most part, has a long history in the empirical or theoretical literature.
Our findings show that knowledge measurements are significantly more predictive, with adjusted R2 values in regressions omitting personality traits reaching at least 90% of the whole model for all outcomes in all tests. We also notice that behaviours like trading turnover are easier to explain than cognitive biases like the disposition effect. With the studied variables, it's also difficult to anticipate investment performance, which is the sum of actions and biases.
Multivariate linear regressions are used in this research. We report a test of the joint importance of those sets of variables for each conceptual system (grouping of knowledge and personality variables). To assess the explanatory power of the two conceptual systems, we additionally report corrected R2.