Date of submission: February 1, 2023; date of acceptance: April 28, 2023. * Contact information: 20619_bidush@kusom.edu.np, MPhil Scholar, Kathmandu University School of Management, PinchheTole, Sasatancha, Balkumari, Lalitpur, Nepal, phone: +977 9841323234; ORCID ID: https://orcid.org/0000-0002-3754-6184. ** Contact information: miragyawali81@gmail.com, School of Management Trib- huvan University, Kirtipur, Kathmandu, Nepal, phone: +977 9849671333; ORCID ID: https://orcid.org/0000-0002-7614-2035. Copernican Journal of Finance & Accounting e-ISSN 2300-3065 p-ISSN 2300-12402023, volume 12, issue 1 Nepal, B., & Gyawali, M. (2023). Behavioral Biases and Portfolio Strategies: Analyzing the Impact on Investor Decision Making in the Nepalese Stock Market. Copernican Journal of Finance & Ac- counting, 12(1), 83–102. http://dx.doi.org/10.12775/CJFA.2023.005 biDush nepal* MPhil Scholar, Kathmandu University School of Management mira gyawali** School of Management Tribhuvan University behavioral biases anD portfolio strategies: analyzing the impact on investor Decision maKing in the nepalese stocK marKet Keywords: behavioral biases, portfolio investors, portfolio strategies, decision making. J E L Classification: G40, G41, G19, O16. Abstract: Our paper explored the impact of demographic variables on the manifesta- tion of behavioral biases among Nepalese portfolio investors as they make investment decisions. Our research analyzed the relationship between age, gender, and experience, and five common biases such as overconfidence, anchoring, herding, loss aversion, and hindsight. Our survey of 132 investors revealed that demographic factors play a role in the presence of these biases, with female investors exhibiting higher overconfidence and anchoring biases, while male investors displayed loss aversion, herding, and hind- sight biases. Younger investors were found to be more overconfident and prone to loss aversion, whereas more experienced investor demonstrated greater overconfidence in their analysis skills. This paper provides crucial insight into the importance of consid- http://dx.doi.org/10.12775/CJFA.2023.005 Bidush Nepal, Mira Gyawali8484 ering demographic factors in addressing investment decision-making biases. It’s worth noting that the responses and sample location may not be fully representative.  Introduction Introduction Behavioral finance delves into psychological factors and cognitive biases to un- cover how thoughts and emotions shape financial choices and affect market prices (Statman, 1999). It seeks to explain market inefficiencies by considering psychological biases rather than dismissing them as random deviations from the efficient market hypothesis (Fama, 1998). The allure of finance lies in its ability to decipher and forecast the sway of emotional decision-making on fi- nancial markets (Sewell, 2007). Behavioral finance also examines various be- havioral biases that affect decision-making (Shefrin, 1998). A bias is a predis- position to make decisions based on underlying beliefs or prejudices, rather than objective facts (Shefrin, 2007). By understanding behavioral biases, in- vestors can make better investment decisions, maximize returns, minimize risk, and have better financial planning (Wamae, 2013; Bashir, Azam, Butt, Javed & Tanvir, 2013). There have been numerous studies that have focused on various behavioral factors such as herding, prospecting, risk aversion, anchoring, overconfidence, loss aversion, framing, and status quo bias (Sukanya & Thimmarayappa, 2015) and other studies have analyzed the impact of demographic factors on inves- tor biases (Jamshidinavid, Chavoshani & Amiri, 2012; Bashir et al., 2013; Bakar & Yi, 2015; Onsomu, 2015) as well as the impact of behavioral biases on port- folio investor decision-making based on demographic factors (Subash, 2012). Despite the numerous studies that have explored the relationship between de- mographic factors, biases, and decision-making in the Nepalese stock market, there have been relatively few studies on biases among Nepalese portfolio in- vestors. This paper is the first of its kind to analyze the influence of overcon- fidence, herding, anchoring, hindsight and loss aversion bias on the decision- making of Nepalese portfolio investors, while also considering impact of age, gender, and experience thus fills a gap in the literature by examining the re- lationship between behavioral biases and demographic factors in the context of Nepalese portfolio investors. The paper examines the following questions: (1) Do age, gender, and experience of investors have a relationship with behav- ioral bias? and (2) What groups of investors (age, gender, and experience) are more susceptible to bias while making investment decisions? BEhavioral BiasEs and Portfolio stratEgiEs… 8585 Literature reviewLiterature review Behavioral finance delves into the psychological underpinnings of financial choices, uncovering the factors that shape financial decision-making. Accord- ing to Sewell (2007), it is the study of how psychology affects the behavior of fi- nancial professionals and the resulting impact on markets. It examines into the ways in which emotions and cognitive errors can shape the actions of individ- ual investors (Kengatharan & Kengatharan, 2014; Alwahaib, 2019). Empirical research, such as that conducted by Bashir et al. (2013) have shown that inves- tors are not always as rational as economic theories suggest, and thus behav- ioral finance aims to explain this. Research in behavioral finance often draws from the study of cognitive psychology, which examines how people process and use information to make decisions. Gitman and Joehnk (2008) suggests that behavioral finance researchers hold the view that investors’ attitude and choices can cause them to excessively react to certain financial data and not re- act enough to others, resulting in illogical decision making and dangerous risk- taking actions. One aspect of behavioral finance is the heuristics theory, which involves us- ing “rules of thumb” or common sense to solve problems and simplify the de- cision-making process (Jordan, Miller & Dolvin, 2012). This can lead to biases and cognitive errors, resulting in predictable, non-optimal choices in the face of uncertain and difficult decisions. Factors such as the availability bias, con- servatism, overconfidence, and herding have all been identified as part of the heuristics theory (Wamae, 2013). Several studies have explored the impact of psychological factors on invest- ment decisions made by investors in various stock markets globally. Some of the behavioral biases that have been identified in these studies include opti- mism bias, overconfidence bias, regret aversion bias, and hindsight bias (Kah- neman & Riepe, 1998). Tripathy (2014) found that investors in the Bhubanesh- war Stock Exchange were influenced by anchoring, overconfidence, regret, and loss aversion biases. Chin (2012) found that conservatism bias, regret, and overconfidence had a significant impact on investment decisions of inves- tors in the Malaysian stock market with no effect of herding behavior. Further, Jain et al. (2022) incorporated a multi-stage scale development methodology, includingextensive literature review and interviews with stockbrokers to de- velop a comprehensive, reliable and valid scale for measuring the behavioural Bidush Nepal, Mira Gyawali8686 biases affecting investors’ decision-making process and found that behaviour- al biases is a multidimensional phenomenon that significantly affects inves- tors’ decisions and has different dimensions, namely, Representativeness Bias, Availability Bias, Market Factors, Anchoring, Herding, Overconfidence Bias, Re- gret Aversion, Mental Accounting, Loss Aversionand Gamblers’ Fallacy. Likewise, other studies have investigated the relationship between individ- ual investor decision making and behavioral biases in various countries (Ros- tami & Dehaghani, 2015; Ady, 2018). Some studies have found that psychologi- cal factors such as overconfidence and herding behavior can have a significant impact on investors’ decision making (Bashir et al., 2013; Wamae, 2013). How- ever, other studies (Kengatharan & Kengatharan, 2014) found that overcon- fidence can have a negative impact on decision making. The impact of herd- ing behavior on decision making has been mixed, with some studies finding a positive impact (Wamae, 2013; Kengatharan & Kengatharan, 2014) and oth- ers finding showed no significant impact (Chin, 2012). Bashir et al. (2013) also found that loss aversion had a significant relationship with investors’ decision making, but with no impact. Furthermore, Calzadilla, Bordonado-Bermejo and González-Rodrigo (2021) analyzed biases in decision-making by ordinary in- vestors while considering socio-economic variables through a systematic re- view and meta-analysis and found that the literature evidence was mixed with that of institutional investors. They further added that the socio-economic evi- dence in the literature review regarding gender, age, studies, and geography was limited and incomplete. There is also a lack of research on the relationship between demograph- ic factors and behavioral biases of portfolio investors in developing countries, particularly in Nepal. Previous studies conducted in Nepal, such as (Adhikari, 2010; Dhungana, Bhandari, Ojha & Sharma, 2020) have focused on the general behavioral finance factors that impact individual decision making, but there is a need for more research on the impact of demographic factors on the behavio- ral biases of portfolio investors. This paper fills this gap in the literature by ex- amining the impact of five basic biases (herding, loss aversion, overconfidence, anchoring, and hindsight bias) on portfolio investors’ decision making in rela- tion to three demographic variables (age, gender, and experience) in Nepal. Based on an examination of existing research and theoretical framework used in this paper, hypotheses were formulated to address the main research question and issue. BEhavioral BiasEs and Portfolio stratEgiEs… 8787 H1: There is a noteworthy association between an individual’s age and the bi- ases that influence their investment decisions. H2: There is a strong connection between one’s gender and the tendency for in- vestor bias. H3: Investors who have more experience are likely to be more or less overconfi- dent than those who lack experience. H4: Younger investors are more or less inclined to show herd-like behavior. H5: Younger investors tend to be more or less anchored in their decision-making compared to those with more experienced investors. H6: Younger investors tend to be more or less to exhibit the loss aversion bias. H7: Young investors tend to be more or less to exhibit the hindsight bias. Research methodsResearch methods This paper used a researcher-administered questionnaire survey to examine the impact of five biases (herding, overconfidence, hindsight, anchoring bias and loss aversion) on portfolio investors based on their age, gender, and ex- perience using various statistical tools and techniques, including descriptive analysis, discriminant analysis, weighted scoring method, chi-square test for independence, t-test, and cross tabulation and the research was descriptive in nature. In order to determine the number of investors in Nepal, the paper col- lected responses from a minimum of 120 respondents using a rule of thumb from Roscoe (1975), ultimately obtaining responses from 132 investors. This paper collected primary data using a questionnaire that was distributed to investors who had invested in multiple sectors. The questionnaire contained two sections, one for demographic variables and the other for behavioral fac- tors, and included questions on five biases (overconfidence, herding, anchor- ing, hindsight, and loss aversion) using a 3-point Likert scale. The sample pro- file was formulated considering age, gender, and number of years of experience of respondents in the stock market. Data were collected from multiple broker- age firms in Kathmandu areas during trading hours by visiting each location and providing assistance to respondents with explanations of the questions as needed in order to obtain the desired number of responses. A pilot study was conducted to ensure validity of the research instrument by identifying and modifying ambiguous or irrelevant information and the questionnaire was pretested with 20 respondents to assess clarity. Bidush Nepal, Mira Gyawali8888 Analysis and resultsAnalysis and results Majority of respondents were between the ages of 26 and 35 (58% of the total population), male (61% of respondents), educated and from a financial back- ground (68% of respondents), self-employed (36% of respondents), and had less than 5 years of investment experience in the Nepalese stock market (53% of respondents). The majority of respondents were income-earning individuals, with 36% being self-employed, 33% being employed, and 30% being students. Only 3% of respondents were unemployed and relying on the stock market as their main source of income. These demographics suggest that the Nepalese stock market is mostly comprised of young, male, financially knowledgeable individuals who are actively earning an income. Likert scale statements that used to measure biases were found reliable, as the Cronbach alpha values for all five biases were above 0.6, indicating consist- ency in the responses. Table 1. Cronbach’s alpha value Statement Noofstatements Cronbach’salpha Overconfidencebias 4 0.785 Lossaversionbias 2 0.741 Anchoringbias 2 0.689 Hindsightbias 2 0.756 Herdingbias 3 0.825 S o u r c e : Compiled by Author. Discriminant Analysis results showed that demographic factors were effec- tive in categorizing investors, with female investors showing higher levels of overconfidence, male investors being more prone to loss aversion, herding, and hindsight biases, and experienced investors exhibiting higher levels of loss aversion, herding, and hindsight biases. On the other hand, young investors showed higher levels of overconfidence and anchoring biases. The results were supported by statistical evidence and p-values, with the p-values for each bias all being less than 0.05 as shown in Table 2, indicating noteworthy disparity in the behavioral patterns of investors with different characteristics. BEhavioral BiasEs and Portfolio stratEgiEs… 8989 Table 2. Discriminant analysis: Group statistics (Panel A) and Equality of group means (Panel B) for gender, Age group and Experience Panel A Overconfidence Loss aversion Hindsight bias Anchoring bias Herding bias Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Gender Male 1.853 0.437 2.094 0.776 2.056 0.595 1.319 0.375 2.383 0.621 Female 2.197 0.449 1.664 0.752 1.731 0.414 1.481 0.542 2.039 0.728 Age Group 15-25 2.375 0.441 1.4 0.503 1.55 0.359 1.375 0.275 1.483 0.653 26-35 2.053 0.403 1.888 0.798 1.908 0.558 1.461 0.528 2.272 0.659 36-45 1.571 0.412 2.5 0.689 2.071 0.427 1.262 0.34 2.635 0.348 Above 46 1.733 0.372 2 0.732 2.333 0.588 1.167 0.244 2.6 0.314 Experience <5year 2.239 0.427 1.514 0.625 1.693 0.484 1.529 0.51 1.919 0.745 >5year 1.706 0.343 2.387 0.704 2.194 0.507 1.218 0.309 2.618 0.335 Panel B Wilks Lambda F Wilks Lambda F Wilks Lambda F Wilks Lambda F Wilks Lambda F Gender 0.872 19.10 0.929 9.928 0.917 11.81 0.969 4.12 0.939 8.474 (0.00) (0.002) (0.001) (0.044) (0.004) Box’s M Test (Gender) 25.557 (0.038) Wilks’ Lambda (Gender) 0.843 (0.001) Age Group 0.728 15.97 0.846 7.751 0.856 7.2 0.945 2.46 0.727 16.04 (0.00) (0.00) (0.00) (0.066) (0.00) Box’s M Test (Age Group) 84.201 (0.004) Wilks’ Lambda (Age group) 0.546 (0.00) Experience 0.678 61.64 0.695 56.93 0.794 33.67 0.882 17.38 0.738 46.26 (0.00) (0.00) (0.00) (0.00) (0.00) Bidush Nepal, Mira Gyawali9090 Panel A Overconfidence Loss aversion Hindsight bias Anchoring bias Herding bias Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Dev Box’s M Test (Experience) 68.986 (0.006) Wilks’ Lambda (Experience) 0.539 (0.00) S o u r c e : Compiled by Author. Bias specific analysis and hypothesis testingBias specific analysis and hypothesis testing Overconfidence was measured through 4 questions with a weighted score cal- culated using a 3-point Likert scale. Anchoring was assessed by 2 questions, while herding was evaluated by 3 questions using the same scale. Loss aver- sion was measured by 2 questions, and hindsight was assessed by 2 questions using the 3-point Likert scale which were then compared to a reference score. The reference score is determined by assuming a sample in which all partici- pants scored at the midpoint of the neutral range on the Likert scale and were found 22. These questions helped determine the extent of biases in the deci- sion-making process of portfolio investors. a) Overconfidence bias First question was: “Are you an expert in stock market?” Second question was:” How would you rate yourself as a trader compared to others in the market?” Third question was: “What do you consider is the cause when the stock you bought goes up in value?” Fourth question was: “To what extent have your in- vestment choices been validated as accurate?” The results from Table 3 shows that the majority of investors have complete knowledge of the Nepalese stock market and a small percentage have no knowl- edge. Both young and experienced investors have similar levels of knowledge. The mean score of both groups is higher than the reference score, indicating a tendency towards overconfidence bias when making investment decisions. Table 2. Discriminant… BEhavioral BiasEs and Portfolio stratEgiEs… 9191 This suggests that there is no significant difference in overconfidence bias be- tween young and experienced investors. The majority believe that their port- folio performance is based on a combination of investment skill and luck. How- ever, both groups also show overconfidence bias, with the majority believing their decisions are correct between 50-80%. Table 3. Cross tabulation table and weighted scoring Less than 5 years More than 5 years Count % within investor type Count % within investor type Overconfidence bias I Yes 3 4.3 10 16.1 Almost 20 28.6 44 71 No 47 67.1 8 12.9 Total 70 100 62 100 Weighted Score 96 126 Mean 16 21 Outcome No overconfidence No overconfidence Overconfidence bias II Above Average 9 12.9 28 45.2 Average 50 71.4 34 54.8 Below Average 11 15.7 0 0 Total 70 100 62 100 Weighted Score 138 152 Mean 23 25.33 Outcome Overconfidence Overconfidence Overconfidence bias III Your Investment Skill 6 8.6 25 40.3 Investment skill and luck 43 61.4 37 59.7 Luck 21 30 0 0 Total 70 100 62 100 Weighted Score 125 149 Mean 20.83 24.83 Outcome No overconfidence Overconfidence Overconfidence bias IV >80% 9 12.9 19 30.6 50%-80% 46 65.7 42 67.7 <50% 15 21.4 1 1.6 Total 70 100 62 100 Weighted Score 134 142 Mean 22.33 23.67 Outcome Overconfidence Overconfidence S o u r c e : Compiled by Author. Bidush Nepal, Mira Gyawali9292 b) Anchoring bias First question was: “Do you set a desired price point before the market opens for the day?”Second question was: “Given the current price of DEF share at Rs 100, after a steep drop from Rs 500, analysts are giving a hold signal. Is it worth in- vesting in the stock in this situation, especially considering its previous high?” Most investors tend to set a specific price range for buying or selling shares, according to the survey results. Over 50% of respondents said that they set a price limit, with 44% of young investors and 71% of experienced investors saying that they do. 22% of young investors and 29% of experienced investors responded “Sometimes”. These results indicate that investors are prone to an- choring bias, as their mean score is higher than the reference score. Both young and experienced investors exhibit this bias, with no significant difference noted between the two groups. Additionally, over 50% of both young and experienced investors consider past information when making buy or sell decisions, with only 1% saying that they do not. This also suggests that both groups are influ- enced by anchoring bias, as their mean score is higher than the reference score. Table 4. Cross tabulation table and weighted scoring Less than 5 years More than 5 years Count % within investor type Count % within investor type Anchoring bias I Yes 31 44.3 44 71 Sometimes 24 34.3 18 29 No 15 21.4 0 0 Total 70 100 62 100 Weighted Score 156 168 Mean 26 28 Outcome Anchoring Anchoring Anchoring bias II Yes 51 72.9 54 87.1 Sometimes 18 25.7 7 11.3 No 1 1.4 1 1.6 Total 70 100 62 100 Weighted Score 198 177 Mean 31.67 29.5 Outcome Anchoring Anchoring S o u r c e : Compiled by Author. BEhavioral BiasEs and Portfolio stratEgiEs… 9393 c) Herding bias First question was: “Will you put your money in a stock even if your own assess- ment of it does not align with the views of a renowned expert in the financial news sector? ”Second question was: “Would you invest in company X’s stock if you had only a basic understanding of it, but saw that many of your peers were investing in it? ”Third question was: “Whose judgment do you trust more?” The results of a survey on investor behavior in Nepal showed that among young investors, 49% would not invest in a stock if it differed from a well-known expert’s valuation and 46% said maybe. In contrast, 69% of experienced inves- tors responded “Maybe” and 21% responded “Definitely”. The data indicates that young investors are more prone to herding bias than experienced investors. The results also showed that over 50% of young investors would invest in a stock de- spite limited knowledge, but most experienced investors would not. The majori- ty of respondents, both young and experienced, trusted their own judgment over that of the media or experts. The results suggest that Nepalese portfolio inves- tors do not blindly follow the actions of others, but rather analyze investment op- tions carefully before making decisions. However, young investors are more like- ly to be influenced by herding bias compared to experienced investors. Table 5. Cross tabulation table and weighted scoring Less than 5 years More than 5 years Count % within investor type Count % within investor type Herding bias I Never 34 48.6 6 9.7 Maybe 32 45.7 43 69.4 Definitely 4 5.7 13 21 Total 70 100 62 100 Weighted Score 170 117 Mean 28.33 19.5 Outcome Herding No Herding Herding bias II Yes 32 45.7 1 1.6 Maybe 0 0 0 0 No 38 54.3 61 98.4 Total 70 100 62 100 Weighted Score 198 177 Mean 31.67 29.5 Outcome Herding No Herding Bidush Nepal, Mira Gyawali9494 Less than 5 years More than 5 years Count % within investor type Count % within investor type Herding bias III Media/Expert 26 37.1 6 9.7 Friend/Relative 11 15.7 2 3.2 Self 33 47.1 54 87.1 Total 70 100 62 100 Weighted Score 133 76 Mean 22.17 12.67 Outcome Herding No Herding S o u r c e : Compiled by Author. d) Loss aversion bias First question was: “Do you tend to stick with a bad stock despite your origi- nal investment decision being wrong, in the hopes of a recovery? ”Second ques- tion was: If you purchased stock in XYZ Hydro based on the belief that its value would increase in the future, but the stock price instead decreases due to a top management conflict, what would you likely do? The results of a survey on investor behavior show that over 50% of young investors hold onto losing stocks in hopes of a turnaround, while the majority of experienced investors do not. Young investors are also more likely to be in- fluenced by loss aversion bias. The data shows that 73% of young investors and 37% of experienced investors would hold onto a stock whose value decreased due to top management conflict. The results suggest that young investors with less than 5 years of experience are more likely to be influenced by loss aversion bias compared to experienced investors. Table 5. Cross… BEhavioral BiasEs and Portfolio stratEgiEs… 9595 Table 6. Cross tabulation table and weighted scoring Less than 5 years More than 5 years Count % within investor type Count % within investor type Loss aversion bias I Always 38 54.3 4 6.5 Sometimes 22 31.4 21 33.9 Never 10 14.3 37 59.7 Total 70 100 62 100 Weighted Score 168 91 Mean 28 15.167 Outcome Loss aversion No loss aversion Loss aversion bias II Hold the stock 51 72.9 23 37.1 Buy more 8 11.4 1 1.6 Sell the stock 11 15.7 38 61.3 Total 70 100 62 100 Weighted Score 180 109 Mean 30 18.167 Outcome Loss aversion No loss aversion S o u r c e : Compiled by Author. e) Hindsight Bias First question was: When your portfolio performs worse than expected, do you feel like you knew it would happen and that you should have sold some of your stocks? Second question was: “If someone had informed you in 2006 or 2007 that a financial crisis was going to occur the following year, would you have be- lieved them?” The study showed that when portfolios underperform, many Nepalese in- vestors experience regret over not selling their stocks. 67% of young investors and 29% of experienced investors said they frequently feel this way, while 30% of young investors and 49% of experienced investors said they sometimes do. This highlights a false sense of security in investment decision-making. The re- sults also revealed that young investors with less than 5 years of experience are more likely to be influenced by hindsight bias, as indicated by their lower mean score compared to the reference score. On the other hand, experienced investors are less prone to this bias. The results of another question showed that 64% of young investors and 48% of experienced investors would have an- swered “Maybe” to a hypothetical scenario, while 19% of young investors and 48% of experienced investors would have answered “No” and 17% of young in- Bidush Nepal, Mira Gyawali9696 vestors and 4% of experienced investors would have answered “Yes”. This fur- ther supports the idea that young investors are more likely to be influenced by hindsight bias in their investment decisions compared to experienced investors. Table 7. Cross tabulation table and weighted scoring Less than 5 years More than 5 years Count % within investor type Count % within investor type Hindsight bias I Often 47 67.1 18 29 Sometimes 20 28.6 30 48.4 Never 3 4.3 14 22.6 Total 70 100 62 100 Weighted Score 184 128 Mean 30.67 21.33 Outcome Hindsight No Hindsight Hindsight bias II Yes 12 17.1 2 3.2 Maybe 45 64.3 30 48.4 No 13 18.6 30 48.4 Total 70 100 62 100 Weighted Score 139 96 Mean 23.167 16 Outcome Hindsight No Hindsight S o u r c e : Compiled by Author. Chi-square testChi-square test Investment biases were analyzed in both experienced and young investors with tests conducted at 95% confidence interval. The findings reveal that while experienced investors tend to have more overconfidence, resulting in riskier investments and poor portfolio performance, young investors display great- er anchoring and herding biases, causing them to make decisions based on the crowd and past outcomes, leading to loss aversion and hindsight bias. These results suggest that young investors are more prone to various biases and face greater risks in their investment portfolio. BEhavioral BiasEs and Portfolio stratEgiEs… 9797 Table 8. Chi-square test Overconfidence bias Chi-square value df asymp. Sig. (2-sided) Q1 Vs Experience 40.086 2 0 Q2 Vs Experience 23.405 2 0 Q3 Vs Experience 32.731 2 0 Q4 Vs Experience 15.576 2 0 Anchoring bias chi-square value df asymp. Sig. (2-sided) Q1 Vs Experience 17.691 2 0 Q2 Vs Experience 4.457 2 0.108 Herding bias Chi-square between: value df asymp. Sig. (2- sided) Q1 Vs Experience 25.587 2 0 Q2 Vs Experience 34.105 1 0 Q3 Vs Experience 23.401 2 0 Loss aversion bias Chi-square test value df asymp. Sig. (2-sided) Q1 Vs Experience 42.73 2 0 Q2 Vs Experience 30.544 2 0 Hindsight bias Chi-square test value df asymp. Sig. (2-sided) Q1 Vs Experience 21.651 2 0 Q2 Vs Experience 16.439 2 0 S o u r c e : Compiled by Author. t-test for individual biasest-test for individual biases The responses were analyzed based on investors’ experience using weighted scoring and chi-square tests. The results were inconsistent and 3-point Likert scale responses were combined to form 5 variables highlighting biases. A t-test was then conducted to compare the mean differences between investors with less than 5 years of experience and those with more than 5 years of experience. We tested hypotheses again using these combined variables. Bidush Nepal, Mira Gyawali9898 Table 9. t-test Biases t Df Sig.(2-tailed) Overconfidence 7.851 130 0.00 Anchoringbias 4.170 130 0.00 Herdingbias -6.802 130 0.00 Lossaversion -7.545 130 0.00 Hindsightbias -6.802 130 0.00 S o u r c e : Compiled by Author. The t-test results indicate a significant difference in the mean of behavioral bi- ases between young and experienced investors. Young investors tend to have anchoring bias, while experienced investors have poor decision-making. Addi- tionally, younger investors are susceptible to herding and loss aversion, while experienced investors are not. The results support the hypothesis that inves- tor experience influences exposure to behavioral biases in the Nepalese stock market. DiscussionDiscussion The paper uncovered a link between demographic variables and behavioral bi- ases and revealed a role in the manifestation of behavioral biases in Nepalese portfolio investors. Group statistic and Equality of group mean testindicated that female investors tend to exhibit higher levels of overconfidence and an- choring biases, while male investors tend to exhibit higher levels of loss aver- sion, herding, and hindsight biases. This finding is consistent with the work of Levišauskaitė and Kartašova (2011) and Lee, Miller, Velasquez and Wann (2013), but contradicts previous research by Jaiswal and Kamil (2012), Onso- mu (2014), and Willows and West (2015) who found that men were more over- confident than women. Younger investors tend to exhibit more overconfidence and loss aversion biases, while experienced investors tend to be more overcon- fident, aligning with research by Lin (2011), Zaidi and Tauni (2012), Murithi (2014) but contradicting the findings of Qadri and Shabbir (2014) who found no relationship between experience and overconfidence, and Bashir et al. (2013) BEhavioral BiasEs and Portfolio stratEgiEs… 9999 who found experienced investors to have high herding behavior. Younger inves- tors tend to exhibit more herding, hindsight, anchoring, and loss aversion bias- es, as found by Lin (2011) and Shusha and Touny (2016), but going against the findings of Dhar and Zhu (2006) who found that experience reduces exposure to loss aversion bias. Similarly, Kanojia, Singh and Goswami (2022) also report- ed no clear evidence of herding behavior in the Indian stock market, largely due to the dominant role played by institutional investors and the relatively low level of participation by individual investors. However, a study by Kartini and Nahda (2021) conducted in Indonesia discovered that cognitive biases such as anchoring bias, loss aversion, overconfidence bias, and herding behavior have a significant impact on investors’ decision-making processes. Wilks’ Lambda was used to determine the relationship between age, gender, and experience with the overall set of biases. The analysis showed that there is a significant relationship between these variables and the biases exhibited by portfolio investors. This finding is consistent with the research of Bakar and Yi (2015) but contradicts the findings of Lee, Wang, Kao, Chen and Zhu (2010) and Gloede and Menkhoff (2011) who found that age and gender do not significantly affect investment behavior.  Conclusion Conclusion The paper explored the impact of demographic factors on investors’ behavio- ral biases in Nepal’s stock market. It was found that gender, age, and experience can impact the biases exhibited by investors, with females showing more over- confidence and anchoring biases, and males being affected by loss aversion, herding, and hindsight biases. Younger investors exhibited more herding, hind- sight, anchoring, and loss aversion biases, while experienced investors showed higher levels of overconfidence. This suggests that while experienced inves- tors may have an overestimation of their abilities, young investors may be more prone to being influenced by biases in their investment decisions. The paper highlights the need for investors, both young and experienced, to be mindful of their biases and how they may impact their investment de- cisions. By gaining a deeper understanding of these biases, young investors can improve their portfolio outcomes, while experienced investors can main- tain their edge in the market. The findings of this study can also be used by financial advisors to tailor portfolios for their clients, by security analysts to Bidush Nepal, Mira Gyawali100100 make informed recommendations, and by financial strategists to make accu- rate market forecasts. Overall, the paper underscores the importance of behav- iorally-informed investing in the Nepalese stock market. 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