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Within participant consistencies

  1. 1. Predictors of investment decision making behaviour1 Final report of research undertaken with support of The Ontario Problem Gambling Research Centre Submitted October 2002 by Warren Thorngate and Alieh Rajabi Carleton University Mailing address: Warren Thorngate, professor Psychology Department, Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 Office: (613) 520-2600 x 2706 e-mail = warrent@ccs.carleton.ca Running head: Investment Copyright 2002 by Warren Thorngate. All rights reserved. 1 The research reported here was funded by a 2002 Level I Research Award from the Ontario Problem Gambling Research Centre. The authors are grateful for the Centre’s support.
  2. 2. Investment 2 Summary Three experiments were conducted to explore some of the demographic and attitudinal variables that might affect stock market investment behaviours. In the first experiment, participants were asked to buy and sell shares in any of three companies over a period of 60 simulated trading days after completing a background questionnaire. The share prices of these companies were determined by experimentally manipulated random walks: one stock price randomly walked upward over 60 days, one randomly walked downward, and one walked more variably around its original price. Two-thirds of participants lost money in the market, even though a money winning strategy existed, primarily because they failed to sell their shares in the losing and volatile companies, and failed to buy more shares in the rising company. The frequency of trading and the number of shares per trade were modestly correlated with self reports of liking for gambling, risk taking, saving and spending money, interest and knowledge of stock market investing. Men also showed more boldness in their trading habits, buying and selling more shares per trade than did females. In Study 2, participants completed three simple stock market simulations on three separate days: one simulated a placid stock market, one simulated a volatile market and one simulated an mixture of the two. Each simulation also allowed participants to draw on a line of credit to purchase more shares. Indicators of their investment behaviour showed high consistency from one simulation to the next, and no evidence of improvement in investment performance. No individual differences emerged in the tranquil market, but several emerged in the Volatile market; most paralleled the individual differences found in Study 1. Persons who liked to gamble drew more often than others on their line of credit in order to purchase stock shares. Males showed the same tendency far more often than did females. In Study 3, the participants in Study 2 returned to undertake a commercial, complex and much more realistic stock market simulation based on real, historical, New York Stock Exchange market data. Indicators of their behaviour were less often correlated with background questionnaires, and sex differences were attenuated. However, high correlations were obtained between investment behaviour indicants from the three simple simulations of Study 2 and the equivalent indicants of the complex simulation of Study 3. The results suggest that simple stock market simulations might be at least as useful as questionnaire items for classifying different investors and predicting their investment behaviours in real stock market activities.
  3. 3. Investment 3 Predictors of investment decision making behaviour Judging from the ubiquity of stock market quotes and averages, and from the frequency of news items in all media, stock market investing is a popular and newsworthy activity. Statistics Canada (2002) indicates that about 15% of the Canadian public has at least some form of stock investments, receiving about $24 billion in annual dividends. During the 1990s the widespread growth of stock prices seemed to generate the belief that investing in stocks was a guarantee of wealth. Recent news of the worldwide fall of stock prices reminds us that stock market investing can be a risky activity. Indeed, investing in the stock market has many parallels to gambling and games of chance. All require money to play. All confront the investor or player with uncertain outcomes that have a generally negative relation between the chances winning and the amount won. All stimulate superstitious thinking and the creation of schemes promising to reduce risk and increase wealth. All generate their share of a few fortunate players who become rich and many more unfortunate players who lose a large proportion of what they paid to play. There is a large research literature related to gambling and investment. Some of this research attempts to profile those who gamble, searching for demographic, socio- economic and attitudinal correlates of gambling (see Wildman, 2002). A smaller body of investment research seeks to profile those who invest in stocks, bonds or other financial options (see Hilton, 2001; Slovic, 1972 for reviews). A separate body of laboratory research examines features of uncertain situations that contribute to maladaptive choices, either alone or in interaction with personality and attitudinal variables (for reviews, see Hogarth, 1987; Lopes, 1994; Plous, 1993). But there is scant experimental research specifically examining investment behaviour. Most of the small experimental literature on investment decision making can be found in the emerging area of behavioural finance, and draws heavily from the related area of decision making psychology. Moore, Kurtzberg, Fox and Bazerman (1999), for example, argue that what psychologists call the illusion of (personal) control, anchoring and framing effects bias investors to prefer expert-managed or self-managed mutual funds over funds based on common market indexes such as the S&P or the Dow Jones average, even though the latter generally do better than the former. They support their argument with results from a mutual fund investment simulation given to business students that showed consistent overestimation of fund performance. Thaler, Tversky, Kahneman & Schwartz (1997) demonstrated that business students prefer investing in mutual funds with a smaller expected return if the variability of that return is lower, most often because they are more sensitive to loss than gain (see Kahneman & Tversky, 1979) and because better performing funds with greater variability results in more frequent short-term losses. Such results begin to inform us of the biases that can affect some kinds of investment decisions. Yet, as with all new areas of research, numerous questions remain. For example, though rarely addressed or reported in the literature, individual differences in
  4. 4. Investment 4 investment behaviour almost certainly exist. Some people are likely to be more cautious than others; some are likely to show more biases than others; some are likely to be more successful in their investment outcomes. Are such individual differences related to personal background, attitudes, financial or investment experience? Barber and Odean (2001) report that men trade their stocks 45% more often than do women, a finding they attribute to the tendency for men to be overconfident in their judgements. Liais, Hilton, Mazurier and Pouget (2002), report that overconfident traders in an experimental trading game show lower trading profits than do less confident ones, and that high self-monitors (people who are especially attentive to what other people are doing) show high trading profits than do low self-monitors, presumably because high self-monitors give more consideration to the investment behaviour and motives of others. Such results support the belief that predictable individual differences do exist, but give us only a small glimpse at what these differences might be. The following research attempts to investigate further what kinds of individual differences might affect investment decision making. Though exploratory, we are especially interested in the links between (1) attitudes towards money, risk and gambling, interest in and knowledge of stock market investing, patience and frustration tolerance, and (2) stock market investment behaviour. We are also interested in how consistent investment behaviour is over time and tasks. To assess these relations, we asked male and female volunteers to answer several background and attitudinal questions, then trade stocks in a simulated stock market. Individual differences could then be explored. Study 1 The primary purpose of Study 1 was to develop a realistic stock market simulation that would allow participants to buy and sell shares in stocks advancing and declining in ways preset by the experimenter, and that would record participants’ investment activity for later analysis. The second purpose was to determine how a sample of participants behaved in the simulation and to relate individual differences in their behaviour to answers on a background questionnaire. Three versions of a stock market simulation computer programme were written by the senior author and evaluated by seven students and colleagues. Their comments about the investment task, instructions, layout and clarity of information on the screen guided improvements in each version. It became apparent that the investment task should be as simple as possible in order for participants to comprehend it, to learn how to buy and sell stocks, and to engage in trading within the usual 1-hour limit of a volunteer’s attention. As a result, the final version of the simulation asked participants to buy or sell shares in only three companies for a period of 60 simulated trading days (= 12 calendar weeks or one Quarter of a fiscal year). No discounting, short selling or other advanced investment procedures were possible. The only information provided about each company was daily share price. This excluded the possibility of participants seeking background information about company history, financial status, employees, earnings, etc.; decisions could be based only on share price trends. Such simplifications left the simulation little more than a caricature of a real stock market. But the caricature captured many of the important characteristics of a real market and offered an uncomplicated investment task, quickly
  5. 5. Investment 5 understood by participants, from which more complex variations could later be constructed. For this first study, all three companies began the 60 simulated trading days trading at $100 per share. Share prices on each subsequent day were determined by three different random walks, one for each company. One of the three companies randomly walked up in value with relatively small up-and-down fluctuations. Another company randomly walked down in value with minor up-and-down fluctuations. The third, volatile company randomly walked around its original $100 per share value with up-and-down fluctuations about three times as large as those of the other two stocks. This configuration allowed participants to find and follow an optimal investment strategy for maximizing their investment returns: buy shares in the company with steadily rising share prices, sell shares of the company with steadily declining share prices, and avoid the volatile company with shares that neither rise or fall in price. We were interested to learn what proportion of the participants would sooner or later discover and employ this strategy. With little light from previous literature, we had little idea of what individual differences to expect. Because previous research (e.g., Barber & Odean, 2001) indicated large sex differences, we asked participants to note their gender, expecting males to trade more often and to make bigger trades reflecting their overconfidence. We added 32 more items to our questionnaire asking participants their age, self-ratings of various attitudes towards risk, gambling, saving and spending money, stock market knowledge and experience. There were also yes/no/number questions related to current personal finances, including possession of bank accounts, credit/debit cards, loans, jobs, investments, and number or children (perhaps naively assuming participants with children might be less prone to risk). The questionnaire responses allowed us to determine inter-correlations of its items, and to see if any items correlated with several indicators of investment behaviour. Methods Participants. Thirty-three males and 32 females ranging in age from 15 to 57 (Md = 22) volunteered to participate in one of our studies. Five of the participants did not reveal their age; 18 of the remaining 62 participants were over 30 years old. Twenty-seven participants were recruited from the university’s Introductory Psychology course and received course credit for participation. The remaining participants were recruited from other courses and from acquaintances in a local apartment complex. All 67 participants completed the background questionnaire. Thirty-six of these participants also completed the stock market simulation task used in Study 1 and described below; 31 of them were university undergraduates. Twenty-six of the remaining 29 participants subsequently participated in the simulations of Study 2 and 3. Questionnaire. All participants were asked to complete a one-page questionnaire containing 33 items. Two items asked for age and sex. Four asked about stock market investment experience: Had the participant ever invested in the stock market? If so, within the past year? The past month? Had the participant ever invested online (Internet)? Fourteen items asked for personal financial indicators: whether or not the participant had
  6. 6. Investment 6 a chequing account, savings account, debit card, credit card (how many?), a loan (bank, student or from family or friends), a part-time or full-time job, a car, investments (mutual funds, bonds, stocks) and children (how many?). Thirteen items asked for ratings on a scale from 0 = “not at all” to 9 = “very much” of personality self-descriptors, investment and gambling issues, and attitudes about money. These items are shown in Table 1. Table 1. The thirteen rating scale items. Question # Question 1 Are you usually patient? 2 Do you enjoy saving money? 3 Do you enjoy spending money? 4 Do you like to gamble? 5 Are you interested in stock market investing? 6 Are you optimistic about your financial success in the next 10 years? 7 Do you like to take risks? 8 Do you worry about money? 9 Is it important for you to be rich? 10 Are you knowledgeable about stock market investing? 11 Are you easily frustrated? 12 Are you usually confident about what you do? 13 Do you believe that stock market investment is a form of gambling? Investment task. Each of the 36 participants asked to complete the investment task was faced with a simple computer simulation of a stock market, designed in Microsoft Excel and programmed in Visual Basic. At the top centre of the computer screen was a 5x3 matrix layout of information and buttons. Above columns 2-4 were the labels “Shares”, “Price” and “Total”, respectively. To the left of rows 2-4 were the names of the three hypothetical companies: Azco, Boco and Cleo. Rows 1-3 of column 1 showed text boxes showing how many shares the participant currently owned in each of the three companies. Rows 1-3 of column 2 showed the current stock market price of each share of the three companies. Rows 1-3 of column 3 showed the total invested (shares times price) in each company. Rows 1-3 of column 4 had three buttons labelled “Buy” and rows 2-4 of column 5 had three “Sell” buttons, one buy/sell pair for each company. Pushing one of the six buttons would bring up a dialogue box asking the participant “How many shares of [Azco/Boco/Cleo] do you want to [Buy/Sell]?” with a text-entry box underneath. Entering a number in the box and pressing the “OK” button caused the computer programme first to check for sufficient funds (buy) or shares (sell). Diplomatic error messages were given to the participant if the funds or shares were insufficient. Otherwise the computer would modify the relevant text boxes (e.g., shares of Azco and total invested in Azco) with the updated information. The money needed to buy shares, or obtained when shares were sold, was withdrawn or deposited in a bank account. The label “Bank account” appeared to the left of row 4; row
  7. 7. Investment 7 4 column 3 showed a text box containing the current bank balance. The account was updated after each buy/sell transaction, and was never allowed to go below zero. The label “Net worth” appeared to the left of row 5. Row 5, column 3 contained a text box showing the participant’s net worth: the sum of the three totals invested in shares plus the bank account balance. The net worth box was also updated after each transaction. Centred to the right of the six buttons was a large red button labelled “Next day”. Pushing this button would advance the stock market simulation to the next simulated trading day. Each new day changed the share price of the three companies which, in turn, changed the entries of the boxes showing the total invested in each company and the net worth. Below the matrix in the centre of the screen was a share price chart showing days 1-60 on the abscissa and share price from $0 to $200 on the ordinate. The chart plotted the evolving share price fluctuations of the three companies. Each time a participant clicked the Next Day button, the chart would add lines from the previous day’s share prices to the current prices. In this way, participants could easily see the upward trend of one company, the downward trend of a second company, and the large share price fluctuations of the third. A screen shot of the investment task is shown in Figure 1. Figure 1. Screenshot of the investment decision making task. The computer programme recorded eight pieces of information for each of the 60 simulated trading days: the number of shares of each company owned after trading that
  8. 8. Investment 8 day, and the price of each share that day, the amount of money in the bank, and the net worth. From these numbers several indices of investment behaviour were derived, explained in the Results section below. Procedure. Participants were brought to the laboratory one at a time, greeted by the experimenter, asked to read and sign an Informed Consent form (all did), then asked to complete the Background Questionnaire. After completion, each participant was seated in front of the computer, shown the first screen of the investment simulation and read the simulation instructions. The instructions presented a hypothetical scenario. Each participant was asked to pretend that his/her aunt had left him/her an inheritance currently worth $10,000: 20 shares of stock in Azco at $100 per share; 20 shares in Boco at $100 per share; 20 shares in Cleo at $100 per share, plus $4,000 in a bank account. These numbers were shown in the relevant text boxes on the computer screen. The participant was told that during the next 60 days of trading, he/she was to try to gain as much money as possible by buying and selling stocks. The mechanics of buying and selling were then explained. To motivate the participants, all were told that they would be paid $1 for every $1,000 they had at the end of 60 simulated trading days, rounded up to the nearest $1,000. Thus, a participant ending with $13,273.48 would be paid $14. Participants were encouraged to ask questions before beginning and all questions were answered. Almost all participants completed the investment simulation task in less than 30 minutes. Following completion, each participant was informally interviewed by the experimenter about reactions to the investment simulation and about investment strategies. In order to control for company position effects, the assignment of company name to the advancing, declining or volatile random walk was counterbalanced. Six of the participants saw Azco rise, Boco fall and Cleo oscillate. Another six saw Azco fall, Cleo rise and Boco oscillate. The remaining four permutations were also employed, each seen by six participants. Results Background questionnaire. We began our investigation of questionnaire responses by correlating all item pairs of the 13 rating-scale items (0 = not at all… 9 = very much) of the background questionnaire with data from 65 participants: the 36 participants who completed the investment task and 29 who had previously completed only the questionnaire. Twenty of the 78 pairs were reliably correlated. They are shown in Table 2.
  9. 9. Investment 9 Table 2. Reliable correlations among rating scale questions. Question Correlates with question (r#): 1. Patient? r12= 0.32 2. Enjoy saving money? r3= -0.31, r4= -0.32, r13= -0.32 3. Enjoy spending money? r2= -0.31, r4= 0.35, r8= 0.32, r13= 0.37 4. Like to gamble? r2= -0.32, r3= .35, r5= 0.27, r7= 0.40, r13=0.29 5. Interested in stock market investing? r4= 0.35, r6= 0.48 , r9= 0.34, r10= 0.50 6. Optimistic about financial success? r5= 0.48, r8= -0.26, r10= 0.30, r11= 0.28, r12= .25 7. Like to take risks? r4 = 0.40 8. Worry about money? r8= 0.37, r6 = -0.26, r12 = -0.28 9. Important to be rich? r5= 0.34, r10 = 0.46 10. Knowledgeable about investing? r5= 0.50, r6= 0.30, r9= 0.46 11. Easily frustrated? r6= -0.28 12. Confident about what you do? r1= 0.32, r2= 0.28, r3= 0.30, r6= 0.25, r8 = - 0.28 13. Stock market a form of gambling? r2 = -0.32, r3= 0.37, r4= 0.29 N.B., r>0.25: p<0.05; r>0.32: p<0.01; df =64 As seen in Table 2, the statistically reliable correlations were moderate at best, indicating that the questions reflected somewhat different aspects of attitudes towards money and risk. A factor analysis (Principal Components with Varimax rotation) strengthened this indication. Though the three most prominent components could together account for only 49.8% of the variance, and three more could bring the total up to no more than 73%, the first three factors nicely separated question items. Items loading high positive on the first factor included interest in the stock market (Question 5), knowledge of the stock market (Q10), being rich (Q9), and optimism about financial success (Q6). Items loading high on the second factor included liking to gamble (Q4), liking to spend money (Q3), liking to take risks (Q7) and equating investment with gambling (Q13); liking to save money loaded high negative on this second factor. Confidence (Q12) scored high positive, and worry about money (Q8) scored high negative on factor 3. Patience (Q1) and easy frustration (Q11) loaded at opposite ends of factor 4. Forty-eight participants reported never investing in the stock market; 16 participants reported that they did. Of these 16, 12 reported investing in the past year and 4 reported investing in the past month; 12 reported investing online. Not surprisingly, those reporting they invested also rated themselves as more interested in investing (Q5 means: 6.8 versus 4.9; t(62) = 2.39, p < 0.02) and more knowledgeable about the stock market (Q10: 5.1 versus 3.4; t(62) = 2.12, p<0.05) than did non investors. The investors were also less worried about money (Q8: 3.7 versus 5.9; t(62) = 2.97, P < 0.01). Online investors showed parallel differences in investment interest and stock market knowledge. In addition, they enjoyed spending money less than did non investors (Q3: 4.9 versus 6.5;
  10. 10. Investment 10 t(62) = 2.20, P < 0.03) but liked to take more risks (Q7: 6.8 versus 5.1; t(62) = -2.60, p < 0.02). There was only one sex difference in the mean ratings of the 13 questionnaire items above: Males rated themselves as significantly more knowledgeable about stock market investing (mean rating = 5.27 on question 10) than did females (mean = 2.32), t(62) = 5.03, p<0.001. Age was correlated with only one indicant: how many, from 0 to 3, of stocks, bonds or mutual funds a participant had invested, r(58) = +0.29, p < 0.03. Fifty-six participants reported they had a chequing account; nine reported they did not. Those who had an account rated saving money (Q2) as more enjoyable than did those who did not (8.1 versus 6.4, t(63) = 2.14, p<0.05) and also rated themselves as more knowledgeable about the stock market (6.2 versus 3.5, t(63) = 2.97, p<0.01). Forty-five participants reported having a savings account; 19 reported not having one. The 45 participants who reported having a savings account rated the importance of being rich (Q9) higher than the 19 who reported not having one (6.5 versus 5.0, t(62) = 2.27, p< 0.03). Twenty-three participants reported having no credit card, 28 reported having one and 14 reported having more than one (one participant reported 7). The number of cards was negatively related to enjoyment of saving money (Q2; r(64) = -0.31, p<0.02) and to worry about money (Q8; r(64) = -0.25, p<0.05), positively related to enjoyment of taking risks (Q7; r(64) = +0.30, p<0.02). Thirty-seven participants reported having no student, bank or personal loan; 19 reported having one loan, 9 reported two, one reported three. The number of loans had no reliable correlation with any of the 13 rating scales. Thirty-five participants reported no investments in stocks, bonds or mutual funds; 20 participants reported having one of these investments, five reported two, five reported all three. The number of investments was positively correlated with interest in the stock market (Q5; r(64) =+0.28, p<0.03) and with optimism about future financial success (Q6; r(64) = +0.25 p<0.05). It was negatively correlated with worry about money (Q8; r(64) = -0.52, p<001) and the belief that stock market investing is gambling (Q13; r = -0.26, p<0.04). Nineteen participants reported owning a car, 46 reported not owning one. Those who did were more optimistic about their financial future (Q6 means: 7.8 versus 6.6; t(63) = 2.40, p<0.02) and less worried about money (Q8: 5.8 versus 4.1; t(63) = 2.41, p<0.02). Twenty-nine participants reported having no job, 26 reported having a part-time job, and 10 reported having a full-time job. Those with a part-time job reported liking to gamble more (Q4 mean = 4.2) than those without a job (2.8) or those with a full-time job (1.8), F(2,62) = 3.45, p < 0.04. Only eight participants reported having children; five had one child and three had two. Those with children reported less enjoyment spending money (Q3: 4.6 versus 6.5; t(63) = -2.14, p < 0.04), less liking of gambling (Q4: 3.5 versus 1.0; t(63) = 2.51, p < 0.02), and rated themselves as less easily frustrated (Q11: 4.0 versus 2.2; t(63) = 2.01, p < 0.05) than did those without children.
  11. 11. Investment 11 Investment behaviour. The price trends of the three stocks -- one rising, one falling, one unpredictably mixed -- allowed participants to pursue an optimal investment strategy for increasing their wealth: sell shares in the falling company as soon as possible, sell shares in the unpredictable company whenever it goes below its original price, and invest all money in rising company shares. If this strategy were pursued in the early days of trading, a participant could expect to double his/her money over the 60 trading days. Few participants pursued it. Thirteen participants gained money by the end of 60 trading days, with their net worth showing an average gain of $3,220.27 on their original $10,000 equity. The most successful participant gained $7,159.58, ending the 60 days with an impressive $17,159.58. The remaining 23 participants lost money, losing an average of $1,632.14 on their $10,000 equity. The least successful participant lost $5,526.43, ending the 60 days with only $4,473.57. Because five participants sold all their shares in the last three days of trading, records of shares owned on day 60 would be skewed by these “cash ins”. We therefore examined shares owned on day 56, the last day when all participants had at least one share. By day 56, participants had an average of 23.7 shares of the rising stock, 22.9 shares of the falling stock, and 19.4 shares of the volatile stock. There was no significant difference in these averages, F(2,35) = 0.36, p>0.7. There were, however, extremely large individual differences in stock purchases, apparent in the range of shares in each stock and in the money gained or lost at the end of 60 days of trading. For example, the number of shares of rising stocks owned at day 56 ranged from 0 to 68, the number of falling stock shares ranged from 0 to 100, and the number of volatile shares ranged from 0 to 110. As expected by the structure of the simulation, the 13 participants who made money ended trading with more shares in the advancing company (average = 31.3 shares on day 56) than in the declining company (12.8 shares) and volatile company (23.7 shares), F(2,24) = 3.54, p<0.05. The 23 participants who lost money ended trading with more shares in the declining company (average = 27.3 shares) than in the advancing company (15.8) or the volatile company (17.0), though large individual differences obscured the differences, F(2,24) = 1.5, p<0.25. Approximating the optimal strategy, the most successful participant, whose net worth was over $17,000 by day 56, had 68 shares in the rising stock and 0 shares in the falling and volatile stocks. In contrast, the least successful participant, whose net worth was less than $4,500 by day 56, owned 100 shares of the losing stock and none in the rising or volatile stock. The prominent individual differences found in the stock market simulation prompted us to search for what might predict them. We began by examining the relations between responses on the background questionnaire and 25 indicants of investment behaviour. One, of course, was the net worth = the value of stock shares plus money in the bank at the end of the simulation. The remaining 24 indicants were of two general kinds. The first kind concerned the boldness versus timidity of investment activity, for example, the frequency of transactions (buy + sell), the average number of shares bought or sold in a transaction, the percentage of net worth invested in stocks versus held in the bank
  12. 12. Investment 12 account. The second kind of indicant concerned possible biases, for example, the number and proportion of shares of the rising, falling and volatile stocks owned on day 28 and day 56, and the correlations between share price and number of shares owned. Though all participants began with 60 shares of stock, 20 in each of the three companies, males had accumulated more shares halfway through the simulation (mean = 83.1 on trial 28) than had females (68.0), t(34) = 2.13, p < 0.05. The difference became greater by trial 56; males still owned an average of 83.1 shares, but females had reduced their average shares to 50.8, t(34) = 3.71, p < 0.001. Females tended to sell off their shares in the falling company during the second half of the investment period, reducing the average number from 24.4 to 15.2 shares from day 28 to day 56. Males tended to keep their shares in the losing company, owning on average 28.8 shares by day 28 and 31.6 shares by day 56. As a result, males owned significantly more shares in the losing stock by day 56 than did females, t(34) = 2.14, p < 0.04. There was, however, no significant difference in the net worth of males and females at the end of trading. There was also no significant difference in the number of times men (41.9) and women (52.3) traded stocks, t(34) = 1.02, p > 0.3. However, when men bought or sold shares, they tended to buy or sell more per transaction (13.5 shares) than did women (7.0), t(34) = 2.16, p < 0.04. There were no sex differences in the percentage of net worth invested in stock, the number of shares in rising, falling or volatile stocks. The results suggest that men were bolder in their investment behaviour than were women, but no more successful. There were few reliable correlations between the 13 questionnaire rating-scale items (Table 1) and indices of stock market behaviour. None of the items had a reliable correlation with the net worth of participants at the end of 60 days of trading. The more people enjoyed saving money (Q2), the smaller were their average stock transactions (r = -0.43, p < 0.01), including transactions in the rising stock (r = -0.40), transactions in the falling stock (r = -0.54) and transactions in the volatile stock (r = -0.38). The more participants liked to gamble (Q4), the more often they would sell volatile stocks (r = +0.40), and they more they liked to take risks (Q7), the less often they sold shares in the declining stock (r = -0.34). The more important it was for participants to be rich (Q9), the larger would be their average share transaction, r(34) = +0.47, p < 0.001. This was consistent for buying or selling shares in the stock going up (r = +0.42), the stock going down (r = +0.36), and the volatile stock (r = 0.50). The same trend occurred as knowledge of stock market investing increased: the more knowledgeable participants bought and sold more shares in the average transaction (r = +0.48), including more shares in the rising stock (r = +0.40), the declining stock (r = +0.44) and the volatile stock (r = +0.48). Finally, the more confident people were about what they did (Q12) the less often they sold shares in the declining stock (r = -0.35). No other correlations among the 13 rating scales and indices of investment behaviour were statistically significant. Twenty-five t-tests were performed to determine if any of the 25 investment behaviour indices were related to personal history of stock market investing. There were no statistically significant differences between personally investing in the stock market
  13. 13. Investment 13 (yes/no) and any of the 25 indices of investment. Similarly, there were no significant differences in the 25 investment behaviour indices between those who invested within the past year, past month or online and those who did not. Finally, relevant correlations, t-tests and Analyses of Variance were performed to determine if any of the 14 personal financial indicators (bank accounts, credit cards, loans, job, car, investments) had any effect on any of the 25 investment behaviour indicators. The results were quite consistent. None of the personal financial indicators were reliably related to any of the 25 investment indicators. Discussion Three results seem especially notable in this experiment. First, almost two-thirds of the participants lost money by the end of their 60 days of simulated trading. Second, there were very large individual differences in investment behaviour. Third, though several individual differences were reliably related to items in the background questionnaire, they were more closely associated with timidity versus boldness of investment behaviour, indicated by number of share transactions and their size, than by investment biases or success. The sex differences found in this study reflect those found by barber and Odean (2001): males showed more boldness in their investment behaviour than did females. Our participants’ self-reports of confidence showed no relation to investment behaviour, contrary to Biais, Hilton Mazurier and Pouget’s (2002) finding that (over)confidence correlated negatively with trading profits. The pattern of results leaves unanswered two central and related questions. Why do so many people lose money in a situation that, unlike most games of chance, had a profitable solution? What accounts for individual differences in stock market success? It is quite possible that important individual differences in success related to personality or attitudes were missed by our brief questionnaire. The questionnaire was a small net cast to catch factors that might affect investment behaviour. A more extensive questionnaire might catch more factors. Anecdotal evidence from the post-task interview suggests one potentially important factor related to investment success that was insufficiently measured in our questionnaire: investment experience. Though chance plays a role in market investment success, so too does skill. Like all skills, those affecting investment success are likely to improve with feedback and experience. Fewer than a quarter of the investment simulation participants indicated any investment experience at all, and fewer than one-sixth rated themselves above the midpoint on the investment knowledge question (Q10). Those who did showed no greater investment success than those who did not. But for ethical and logistic reasons, our background questionnaire did not ask how successful their real investments had been, nor did it ask whether they used a broker for their stock transactions, nor did it probe the nature of their investment knowledge. Yet several of the participants’ comments to the experimenter following the simulation suggested an important role for personal experience. About half of those refusing to sell
  14. 14. Investment 14 their shares in the losing stock reported they did not want to sell at a loss or believed the share price would soon rebound -- a naïve belief known as the Gambler’s Fallacy. Although all participants found the investment simulation engaging, about one quarter of the participants indicated that they did not really know what to do. One did. The most successful participant confessed that he was the son a stockbroker, and had learned investment strategies from his father. Such anecdotes indicate the important role of learning in the development of investment strategies and outcomes. Can investors improve their performance with instruction or practice? If so, what kind of instruction or practice is best? Study 2 attempted to address the question of practice by requiring participants to engage in three investment simulations, similar to the one used in Study 1. Analysis of their investment behaviour and outcomes could inform us of any improvements or other changes resulting from repeated feedback and experience. It would also allow us to determine if individual differences would be reduced, sustained or amplified as practice was gained. Study 2 Methods Participants. Twenty-six of the original 65 volunteer participants who completed the background questionnaire in Study 1 were recruited for Study 2, 11 males and 15 females ranging in age from 16 to 51 years (Md = 31). Thirteen of the participants were university students; the remainder worked on or off campus. Each participant was asked to visit the laboratory three times, each time to complete one of three investment tasks: A, B and C. One participant ignored instructions to buy and sell shares of stock and simply observed the ups and downs of the stock prices each session. His data, a string of 0s, were not included in the analyses. A second participant found a flaw in the computer programme controlling task B that allowed him to overdraw his bank account by tens of thousands of dollars to pay for shares, and thus generating indicators of his behaviour that were two orders of magnitude greater or less than those of the remaining participants. His data were accordingly removed from any analyses related to Task B. A third participant moved from the city before completing his last task: A.. No data were thus available for him in Task A, but his available data were included in analyses of results for Tasks B and C. Tasks A, B and C. Similar to Study 1, participants in the current study were asked to invest in a simple stock market with a limited range of alternative stocks for a period of 60 trading days. As in Study 1, the task was displayed on a spreadsheet showing a financial snapshot in a matrix, this time in the upper left corner of the screen, and a stock graph in the centre of the screen. In the current study, however, participants could invest in any or all of four companies (versus three in Study 1): W, X, Y and Z. To simplify trading, shares were bought and sold by clicking on spinners, one per company; the up arrow of each spinner would buy shares, the down arrow would sell shares, and instant updates would be shown in the financial snapshot. Figure 2 shows a screen image of Task A at the end of 60 trials. The screen layout for tasks B and C was identical.
  15. 15. Investment 15 Figure 2. Typical ending screen for Task A (low price volatility). The daily prices of each stock were set by random walks, as they were in Study 1. However, unlike Study 1, no stock was deliberately programmed to rise or to fall. Each random walk was as likely to go up on any day as to go down, so the expected gain of each stock over the 60 days of trading was 0. Of course, random drifts did occur; some stocks rose in share value and some declined over trading days, as seen in Figure 2. Each participant saw a different set of four random walks, one walk for each of the four stocks. This did not allow for precise and specific comparisons of participants’ trading patterns, but it did allow us to generalize averaged comparisons across more than one set of price fluctuations. We were interested to investigate in more detail how participants with varying backgrounds would react to relatively stable versus relative volatile markets. As a result, we programmed Task A to show relatively small price fluctuations in its 4 stocks, and programmed Task B to show relatively large price fluctuations in its 4 stocks. Figure 2 illustrates typical fluctuations of stock prices in Task A. For visual comparison, Figure 3 illustrates typical fluctuations of stock prices in Task B. All stock prices in Task A and in Task B began at $100 per share. As can be seen in Figures 2 and 3, all stock prices drifted from this price in unpredictable ways over the course of 60 simulated trading days.
  16. 16. Investment 16 Figure 3. Typical ending screen for Task B (high price volatility) Task C combined two relatively volatile stocks with two relatively stable ones. W and Y were arbitrarily chosen as the high volatile stocks, fluctuating as they did in Task B. X and Z remained as the low volatile stocks, fluctuating as they did in Task A. In order to determine if the starting price of a stock affects investors’ purchase decisions, we started stocks W and X at $100, Y and Z at $150. As a result, task C became its own mini- experiment with a 2x2, Volatility x Starting Price, within-subject design. Each participant was given $10,000 in play money to begin each task (A, B and C), and told he/she could cash in the play money for real money at the exchange rate of $1 real = $1,000 play at the end of the session. Unlike Study 1, all $10,000 were deposited in the bank account at the start of trading; no shares in the stocks were allocated. Also unlike Study 1, we gave participants a line of credit, allowing them to use more money for investment than they had in the bank. By doing so we could examine how often and how much participants went in the red to invest in more shares. When the bank account dipped below zero, its numbers were shown in red (to illustrate, see bank account in Figure 3). Four participants completed Task A first, Task B second and Task C third. Another four completed Task A first, task C second and Task B third. Four more completed Task B first, A second and C third. These and the remaining three permutations controlled for order effects and allowed us to separate the effects of condition (ABC) and practice (1st, 2nd, 3rd).
  17. 17. Investment 17 Procedure. Upon arriving at the laboratory, all participants were first told of the nature of the study and asked to sign an Informed Consent form required by the Department Ethics Committee (all participants did). They were then asked to completed the same background questionnaire used in Study 1. Following its completion, participants were led to the computer and the nature of the stock market investment task was explained. Participants were told to assume that a distant relative had left them $10,000 as an inheritance, and that they were to make as much money as they could by investing some or all of this money each day over 60 days in a stock market that traded shares in 4 companies: W, X, Y and Z. The mechanics of buying and selling shares were then demonstrated on static screens captured from a previous trial run of the experiment, as was the meaning of each number and line in the stock summary box and the share price graph on the screen. All questions were answered before the experiment began. Participants then completed one of the three Tasks (A, B or C), which one depending on a random order table. Participants took between 20 and 40 minutes to complete it. Following completion all participants were asked to sign up for two other days and times to complete the remaining two tasks. Most returned for the second task 2-7 days after the first, and returned for the third task 2-7 days after the second. Seven participants rescheduled or otherwise delayed, giving them 10-22 days between tasks. Results Background questionnaire. Responses to the background questionnaire given by the 26 participants in Study 2 were included in the analyses of the background questionnaire reported in Study 1 (e.g., see Table 2). Separate analyses of these responses are thus as much tests of subset reliability as a sources of new insight. Most of the correlations were quite similar; for example the correlation between ratings of “Are you patient?” (Question 1) and “Are you usually confident about what you do: (Question 12) was + 0.37 in the current study, +0.32 in Study 1. There were, however, a few differences. The correlation between self ratings of patience (Q1) and liking to spend money (Q3) was r(25) = –0.47, p <0.02, in Study 2. The correlation between liking to spend money and liking to gamble was r(26) = +0.46, p<0.01. The correlation between enjoyment of saving money (Q2) and importance of being rich (Q9) was r( 26) = +0.47, p<0.02. Among the 26 participants in Study 2, age was negatively correlated with optimism (Q6), r(26) = - 0.40, p < 0.05. Females rated themselves as enjoying spending money (mean rating = 6.5) more than did males (4.6), t(24) = 2.47, p<0.02. Males rated themselves as being more worried about money (mean = 6.8) than did females (5.0), t(24) = 2.12, p<0.05. As in Study 1, males also rated themselves as more knowledgeable about the stock market (mean = 5.3) than did females (1.7), t(24) = 3.68, p<0.001. Investment tasks: Practice and experience effects. As previously noted, one purpose of Study 2 was to determine if participants changed their investment behaviours or outcomes with practice or experience. Several comparisons were made of stock market investment behaviour and performance across the three experimental sessions in which each subject participated. Recall that one-third of the participants completed Task A in the first session, one third-completed Task A in the second session, and the remainder
  18. 18. Investment 18 completed it in the third. The same was true for completing Task B, and for completing Task C. In order to determine if the sessions increased outcomes, the average worth (stock value + money in bank) was first calculated for each participants in each task, then compared from one session to the next. The average worth of each participants in Session 1 was $10,169.88; in Session 2 it was $10,118.64 and in Session 3 it was $9,667.24. There was no significant difference in these three means, F(2,40) = 0.504, p>0.6. Nor was there any significant correlation between the average price of stocks over a 60 simulated-day trading session and the average worth of participants: Session 1 r(22) = + 0.12, p> .5; Session 2 r(21) = +0.33, p>0.14; Session 3 r(22) = +0.39, p>0.07. It appears that neither practice in the task nor the average state of the stock market reliably improved the investment performance of the participants. We also examined whether practice affected indicators of investment styles or tactics. One such indicator is the number of times a participant either bought or sold shares. It distinguishes participants who, at one extreme, micro-manage their portfolios and those who, at the other extreme, simply “buy and hold.” A second indicator is the average number of shares bought or sold during a transaction. This indicator is sensitive to those who are timid (“I’ll buy 2 shares of W and sell 1 share of X.”) versus those who are bold (“Give me 300 shares of W and sell 200 shares of X!”). On average, participants traded shares 82.1 times in the first session (about 20.5 times for each of the 4 stocks), 71.6 times in the second and 83.6 times in the third. There was no significant difference in these means, F(2,40) = 0.945, p>0.35. Each time participants traded in the first session, they traded an average of 8.2 shares; in the second session they traded an average of 10.1 shares and in the third session traded 8.9 shares. The difference was not significant, F(2,40) = 0.73, p>0.48. In sum, practice or experience did not seem to affect significantly the average frequency of trading or amount traded. Investment task effects. Recall that Task A confronted participants with four stocks whose share prices were relatively stable (e.g., see Figure 2), Task B gave the participants four stocks with relatively volatile share prices (e.g., Figure 3), and Task C gave participants two stable and two volatile stocks. The three tasks did not significantly affect the average worth of the participants. Over the 60 simulated trading days, participants had an average worth of $9,873.93 in Task A, $10,543.80 in Task B, and $9,538.03 in Task C, F(2.40) = 1.84, p>0.17. Nine of the 22 participants (41%) whose data were included in the analyses of Task A had an average worth exceeding their original $10,000 budget; the equivalent figure for Task B was 13 (59%) and for Task C, 8 (36%). We next examined how the tasks might affect the tactics or styles of investment. Neither the number of trades or the average number of shares traded in each trade was affected by the tasks. When faced with a relative tranquil market (Task A), participants traded shares an average of 85.0 times, or about 21 times for each of the four companies. In the volatile market (Task B), participants traded an average of 68.7 times, and in the mixed market (Task C) they traded an average of 84.7 times, F(2,40) = 1.82, p>0.17. In the tranquil market, each trade averaged 9.8 shares; in the volatile market each averaged 9.1 shares, and in the mixed market each averaged 8.9 shares, F(2,40) = 0.20, p>0.80. Once
  19. 19. Investment 19 more, differences in the means were obscured by high variability, indicating that the tasks were sensitive to large individual differences. As previously noted, the design of Task C allowed us to determine if a stock’s starting price ($100 in W & X; $150 in Y & Z) or its volatility (high in W & Y; low in X & Z) affected trading behaviour. Starting price had a significant effect on number of times a stock was traded: $150 stocks were traded more often (mean = 22.8 days of 60) than were $100 stocks (18.8 days), F(1,22) = 8.73, p<0.01. So also did volatility: the two volatile stocks, W and Y, were traded more often (mean = 22.3 days out of 60) than the tranquil stocks (19.3 days), F(1,22) = 4.16, p<0.05. Over the 60 days, more shares were held in the $150 stocks (mean = 11.4 shares) than in the $100 stocks (6.4 shares), F (1,22) = 11.39, p<0.005. However, neither starting price nor volatility had a statistically reliable effect on the average number of shares bought or sold in any trade. Individual differences. Could any of the items in the background questionnaire predict the differences in tactics or styles of investment? Correlations were calculated between the 13 background questionnaire attitude items (“Do you worry about money?”, “Are you easily frustrated?”, etc.) and (1) the number of trades in each task and (2) the average number of shares of traded when a trade was made. Not one of the background questions was significantly correlated with the number of trades in tranquil Task A, and only one background question was significantly correlated with the average number of shares traded in Task A: the more patient participants reported themselves, the smaller was their average trade, r(22) = -0.43, p<0.05. In volatile Task B and mixed Task C, two questions predicted styles/tactics. The importance of saving money was significantly correlated with the number of trades in Task B (r(22) = +0.44, p<0.04) and marginally correlated with the number of trades in Task C (r(22) - +0.40, p<0.06): the more important saving money was to participants, the more often they traded shares. In addition, the more participants admitted to liking gambling, the greater was the number of shares in their average trade: r(22) = +0.57, p<0.01 for Task B and r(22) = +0.58, p<0.01 in Task C. The pattern of results suggest that individual differences are subdued in tranquil stock markets, but come to the surface when markets become volatile. Study 1 reported significant sex differences in the timidity versus boldness of investment behaviour, indexed by the average number of shares bought or sold per trade. A parallel find occurred in the current study. There were no significant sex differences in number or size of trades for Task A. However, in Task B, males bought or sold more shares per trade (mean = 23.1) than did females (6.4), t(21) = 2.14, p<0.05. Males also bought or sold more shares per trade in Task C (mean = 21.2) than did females (7.1), t(21) = 2.11, p<0.05. Again, the volatility of the four stocks in Task B and of two stocks in Task C seemed to bring out these sex differences. Recall that the task of Study 2 allowed participants to maintain a negative bank balance, a “line of credit” in bank jargon. We were interested to know if any background questionnaire items correlated with the use of this line of credit. So we counted the number of trading days (out of 60) each person had a negative bank balance for Task A, Task B and Task C, then correlated these three indicators with background questionnaire
  20. 20. Investment 20 responses. We did the same for the number of days each person had a bank balance less than $1,000 = 10% of the money they had in the bank when they began – a somewhat arbitrary number. Only one rating scale response reliably correlated with the number of days less than $1,000 and less than $0: gambling. In highly volatile Task B, participants who liked to gamble were more likely to let their bank balance dip below $1,000 (r(24) = +0.42, p<0.05) and more likely to let their balance dip below $0 (r(24) = +0.50, p<0.02). Task B also brought out sex differences: males let their bank balance dip below $1000 more often than females (22.0 days versus 7.7 days, t(23) = 2.24, p<0.04) and more often let their balance dip below $0 (14.7 days versus 1.5 days, t(23) = 2.34, p<0.03). Correlations among tasks. Were participants consistent in their investment behaviour from one session to the next? Yes. The correlations of number of trades from one session to the next were all moderately high and statistically significant: r(21) = +0.64 between sessions 1 and 2; r(21) = +0.47 between sessions 1 and 3; r(21) = +0.60 between sessions 2 and 3. The correlations between average number of shares bought or sold in each trade were even higher: r(21) = +0.86 between sessions 1 and 2; r(21) = +0.78 between sessions 1 and 3; r(21) = +0.88 between sessions 2 and 3. There was no significant correlation between the average worth of participants by the end of one session and their average worth in the other sessions: r(21) = +0.34 between sessions 1 and 2; r(21) = -0.02 between sessions 1 and 3; r(21) = +0.06 between sessions 2 and 3. Thus, there were consistent individual differences in investment styles or tactics, but these differences styles or tactics did not affect investment success – presumably because the fluctuations of stocks came from random walks, allowing no style or tactic to succeed more than others. Similar correlations were found among Tasks A, B and C. The number of trades made in Tasks A and B correlated r(24)= +0.61, p<0.005; between A and C they correlated r(24) = +0.50; between B and C they correlated r(24) = +0.75. The average number of shares bought or sold in each trade also showed moderate to high correlations: r(24) = +0.56, p<0.005 between Tasks A and B; r(24) = +0.55, p<0.005 between A and C; r(24) = +0.98, p<0.001 between B and C. Task A was a somewhat weaker predictor of B and C than they were of each other, perhaps again reflecting the individual differences that emerged in volatile markets. Discussion The results of Study 2 give no support to the hypothesis that practice changes the boldness or timidity of investors or improves how well they do in a tranquil or volatile market. When faced with a mixed market (Task C) that offered volatile and tranquil stocks at different entry prices, participants showed a preference for buying more expensive stocks and trading volatile stocks more often. Individual differences in investment behaviour were, as in Study 1, very large, and some were correlated with items in the background questionnaire when the market was volatile. The two most consistent predictors of investment behaviour were gender and gambling. Males bought and sold more shares per trade than did females; they also incurred debt to buy shares much more often than did females. The more that participants liked to gamble, the more
  21. 21. Investment 21 shares they bought and sold per trade and the more often they went into debt in a volatile market. Such relationships indicate that some of the individual differences in investment behaviour can be detected by a few background questions, though not always the questions that seem to have high face validity. Many background questions with high face validity (e.g., “Do you like to take risks?”) showed no relationship to the indicants of investment behaviour explored here. This suggests that more valid questions might be found more by empirical slogging than by flights of conceptual fancy. The results also reveal that individual differences in investment behaviour were moderately to strongly correlated across the three sessions and tasks. Participants who traded shares frequently or infrequently in one time/task tended to do so in the others. Similarly, participants who traded many shares or few shares in one time/task tended to do the same in the others. The correlations among behavioural indicators of investment behaviour from one time and task to another were rather high, most above r = +0.60, more often higher than the correlations between the best background questionnaire items and the investment behaviour indicators. This suggests that more might be learned about real stock market investment behaviour from examining investment behaviour in simulated stock markets than from examining answers to questionnaires. The suggestion inspired Study 3. Study 3 The simulated investment task used in Study 1 and in Study 2 allowed for the usual advantages of experimental control at the expense realism. The task was constructed as a caricature of a real stock market and, though we believe it was adequate as a caricature, it obviously lacked the richness of the real thing. In real stock markets such as the New York Stock Exchange or Toronto Stock Exchange, investors can choose from hundreds of stocks, search for company information, envelop themselves in minute information, buy and sell several times a day. They also use their own money, often a lot of it. We wanted to learn if behaviour in our simulated task would generalize to behaviour in a stock market investment task closer to real markets. We could not reasonably ask participants to invest their own money over several weeks in a real market. Several “play money” simulations using contemporary real market data from the New York Stock Exchange were available for use on the Internet, but all required participants to trade shares day after day for months. We could not ask our volunteer participants to return each day and spend this much time on the task. Instead, we subscribed to a commercial stock market simulation that made use of a wealth of historical market data. Marketsim (www.marketsim.com) allows participants to buy and sell shares in hundreds of stocks in real companies using play money, based on real market and company data from 1996 to 2002. In addition to providing interface components to trade shares, Marketsim also provides facilities for examining trade history information about every stock. Its main advantage is to allow participants to engage in several weeks of trading in a 2-hour session.
  22. 22. Investment 22 Because Marketsim appeared more realistic than did the micro-markets created for Studies 1 and 2, we used it to compare how well (1) the background questionnaire and (2) Tasks A, B and C in Study 2 might predict real market investment behaviour. Indicants of investment behaviour in Marketsim could be related to background questionnaire items. Perhaps more interestingly, they also could be related to indicants of behaviour in the three laboratory simulations of Study 2. Indeed, based on the correlations shown between and among questionnaire items and investment behaviours in Study 2, we expected that behaviours in Tasks A, B and C might predict Marketsim behaviour better than items in the background questionnaire. To examine how well questionnaire items and Task A, B and C investment behaviours could predict Marketsim investment behaviours, we simply asked the 26 participants in Study 2 to return a fourth time for a Marketsim session. Methods Participants. All 26 participants in Study 2 were asked to return to our laboratory to undertake the Marketsim task. Twenty-two of them agreed, but only 18 were able to arrange a suitable time to participate. These 18 participated between 4 and 21 days after completing the three tasks of Study 2. Technical problems with the Marketsim web site prevented three of these participants from completing the task. Three more of the participants did not push the buttons advancing from one trading day to the next, and instead negotiated all their trades during only one day of trading. In the end, only 12 of participants, 6 males and 6 females, successfully completed the Marketsim task. The task. As can be seen at its web site (www.marketsim.com), Marketsim is designed to offer users practice in trading shares on the basis of technical analysis, that is, by making use of real statistical information about the market and individual stocks, massaged in numerous ways. The web site offers screens for research and trading, as well as screens for the investor’s stock portfolio, providing historical share price information in abundance. Most of the Marketsim screens appear busy and complex, a challenge to the second author faced with the task of explaining to participants how to make use of Marketsim features while trading stocks. Participants were asked to imagine that they had received an inheritance of $100,000 just prior to the January 2000 and that they wanted to invest the money in stocks for six- months. Their goal was to make as much money as possible in the stock market during the six-month period from January to June 2000, a time period included in the Marketsim data base. The beginning capital of $100,000 was the highest of three options offered set by Marketsim. To stimulate their imagination, participants were also told that, at the end of six months of trading, the experimenter would tally their net worth, divide by 10,000 and give the result to them in real Canadian dollars, rounded up to the nearest dollar. Thus, a participant who finished the six months of simulated trading with $171,346.72 would be given $18 for his/her effort. Marketsim offers participants the option of advancing one day at a time through market history and trading, or advancing one week at a time. We told our participants to do either or both as they wished. Thus, at one extreme, participants had the option of
  23. 23. Investment 23 romping through 12 months of trading one week at a time; only 52 trading days would then be recorded. At the other extreme, participants had the option of trading every day, considering trades for about 260 days (52 5-day weeks). All participants chose a combination of these options. Marketsim also offers pages of investment advice, based on various mathematical combinations of price statistics. We asked participants to avoid clicking the button/tab labelled “Sim Trade” that produced this advice, and instead to make their own trading decisions based on their beliefs and on any market research they undertook by clicking the button/tab labelled “Research”. All participants complied with this request. The senior author wrote a computer programme to monitor each participant’s Marketsim activity. The programme recorded (1) the name, entry and exit times of each web page a participant visited, (2) the name, number of shares and price of every stock bought or sold and the time at which it was traded. The resulting data files were then converted by a second computer programme to a form compatible with statistical analysis software packages. Procedure. Prior to undertaking the Marketsim, all participants were contacted to arrange a minimum of two consecutive hours for the task. On arrival, each participant was given a 30-minute tutorial on the workings of Marketsim, how to buy and sell shares, how to research the market and individual companies, how to advance to the next trading day. Included in the tutorial was the Marketsim’s own tour of its facilities and how to use them (http://www.marketsim.com/help/tour). Features of Marketsim relevant to our experiment were further explained, a brief and standardized “dry run” of three days was undertaken by the experimenter while the participant watched, and all questions about Marketsim were answered. Participants were then told to pretend they received the $100,000 inheritance and started in Marketsim on the first January 1999 day of market trading. Each participant completed the task in a closet-sized room where they sat alone at a computer with ethernet connections to the Internet, taking from 1.5 to 6 hours to complete the trading task. After finishing, the experimenter asked participants to describe their experiences and strategies, answered any additional questions, paid them according to the amount earned, and thanked them for their participation. Results We derived three major indicators of Marketsim activity from the files of recorded data. They were: • Total number of trades (buy + sell) per trading day • Average number of shares traded per trading day • Average number of research pages viewed per trading day Though we asked participants to stop trading after a calendar year (about 260 trading days), only 4 of the 12 participants did. Participants advanced Marketsim an average of 343 trading days during their laboratory session, about 119 “next day” advances and about 45 “next week” advances. Large individual differences were found; at the
  24. 24. Investment 24 extremes, one participant stopped after 157 trading days, another stopped after 696 trading days. The three indicators above were expressed in units per trading day in order to compensate for these large individual differences in the number of trading days participants considered. Correlations between background questionnaire and Marketsim behaviour. The results were quite clear. Only one background questionnaire item was significantly correlated with any of the three Marketsim behaviour indicators: Liking risks (Q7) was significantly related to the number of pages of research viewed per day, r(12) = +0.58, p<0.05. Many of the correlations showed about the same magnitude as did the statistically significant ones in Study 2, but did not reach significance because of the small sample size. For example, self ratings of liking to take risks (Q7) correlated r(12) = +0.49 with number of trades per trading day. Worrying about money (Q8) correlated r(12) = +.43 with number of trades per trading day, and r(12) = +0.43 with pages or research per trading day. Liking to gamble (Q4) and importance of being rich (Q9) both correlated +0.32 with average number of shares traded per day. Liking to save money correlated r(12) = +0.34 with number of trades per day. These correlations suggest that some of the background questions might be useful for predicting investment behaviour in realistic simulations such as Marketsim, and perhaps in real markets. Only one sex difference was found: males tended to look at more research pages per trading day (mean = 3.4 pages) than did females (mean = 1.3), t(10) = 2.26, p<0.05. Correlations between Tasks A, B and C (Study 2) and Marketsim behaviour. In contrast to the modest predictive powers of the background questions, behaviours in Tasks A, B and C from Study 2 showed relatively high correlations with similar behaviours in the Marketsim task. The total number of trades per day in Marketsim was correlated with total trades in Task A, r(11) = +0.62, p<0.04, and in Task B, r(11) = +0.56, p<0.06 with Task C, r(11) = +0.41, p<0.18. The average number of shares traded per trading day was correlated with shares per trade in Task A r(11) = +0.92, p<0.001, and in Task B, r(12) = +0.62, p<0.04 and in Task C, r(12) = +0.51, p<0.10. The small sample size likely accounted for the marginal significance of some of these correlations. Yet their values suggest a healthy relationship between investment behaviour in the simple and somewhat abstract investment tasks of Study 2 and investment behaviour in the much more complex Marketsim task. None of the investment behaviour indices of Study 2 were significantly correlated with the number of research pages examined each trading day of Marketsim. However, the number of these pages had a good correlation with the number of trades made per trading day, r(12) = +0.74, p<0.01: The more research undertaken in a day, the more trades were made in that day.
  25. 25. Investment 25 Discussion Although scheduling and technical difficulties limited the number of participants in the Marketsim task, the results suggest that only modest relations exist between answers to the background questionnaire items and Marketsim behaviour. To the extent that Marketsim approximates a real world, on-line investment opportunity, this finding leads us to doubt that self-report indicators of individual differences in attitudes towards gambling, spending or saving money, knowledge of the stock market or interest in it, etc. might account for small proportion of individual variations in investment behaviours. Our doubt is in keeping with the oft-cited finding that self-report scales of personality or attitudes rarely have high correlations with observed behaviour (e.g., see Mischel & Peake, 1982; Olson & Zanna, 1993; Petty, Wegener, & Fabrigar, 1997). Nevertheless, given the paucity of our data, it seems prudent to continue research on questionnaire indicators of investment behaviour. Questionnaire items, of course, can themselves vary enormously in their generality (“I enjoy taking risks”) versus specificity (“I plan to invest at least 10% of my income next week in Cognos stock.”). In view of the literature on predicting behaviour from questionnaire replies, it would seem prudent to concentrate on items at the more specific end of the continuum. One step beyond questionnaire items related to specific situations lie analogues of the situations themselves, in our case simple laboratory simulations of a stock market. Though our data are few, we are encouraged by the relatively high correlations they reveal between participants’ investment behaviours in our simulations (Study 2) and their Marketsim investment behaviours. The simulations are much simpler than Marketsim, and were given to most participants 7-14 days before the Marketsim task. Even so, there was remarkably high consistency in the indicators of investment behaviour we examined. The more frequently participants bought and sold shares during our simulations, the more frequently they did so in Marketsim. The same relation held for the number of shares bought or sold per trade or trading day. Which leads us to wonder if simulations might offer a better method of assessing individual differences than questionnaire items, even specific items. Questionnaire items have two possible advantages. First, questionnaires require no batteries; because they can be distributed and completed on paper, they are often more accessible and convenient for investors than a simulation requiring access to computer equipment. Second, the wording of the items might be more easily related to theoretical constructs in psychology (e.g., individual differences in the construct of sensation seeking might easily lead to an item such as “I prefer volatile markets to placid ones”) than would simulation structures. As a result, questionnaire results might allow us more psychological insight into the personality or attitudes of various investors than would a set of behavioural indicators in a simulation Of course, many questionnaires are now administered via computer, and some simulations can be presented on paper. Typical predictions of behaviour based on a simulation usually require nothing more than assuming people will behave similarly in
  26. 26. Investment 26 similar situations, an assumption almost devoid of psychological insight. Yet theories of similarity do address psychological issues, as do theories of cross-situation consistency (e.g., see Mischel & Peake, 1982; Tversky & Gati, 1978). In addition, not all occasions require psychological insight in order to be useful. If we wish to diagnose the behaviours of an investor or predict his/her behaviours in various markets, a simulation leading to good actuarial predictions might be preferred to a theoretically justified questionnaire making bad predictions. In short, reasons to prefer questionnaires to simulations as diagnostic or predictive instruments might not be as compelling as they first appear. Indeed, there is at least one good reason to develop simulations rather than questionnaires as diagnostic instruments of investment behaviour. Simulations afford opportunities to manipulate the markets they simulate in order to determine investors’ reactions to different “what if…” conditions. How would different investors react to a sudden upswing in the market? To a sudden downswing? To increasing or decreasing volatility? We could, of course, add such questions on a questionnaire, noting the risk of creating a questionnaire of Russian Novel proportions. But it might often be more efficient and effective to observe behaviour when these conditions are simulated. Conclusion We began our studies with the limited goal of developing a simple simulation of a stock market that could be used as one means of validating items on a simple background questionnaire. We end the studies with evidence that some of the background questionnaire items -- especially those about gambling and risk, saving, spending and worrying about money, being rich, interest and knowledge of the stock market – do show consistent, if modest, correlations with indicators of investment behaviour from the simulations. We also end with evidence that a few of the background questions also have modest correlations with behaviour in a much more realistic and complex stock market simulation, and that behaviour in the simple simulations predicts of behaviour in the complex one rather well. Much remains to be done. We should like to expand the background questionnaire and integrate it in a single computer programme or web site with a series of simulations, each generating different market conditions, ultimately to automate the collection and analysis of background and simulation data. We should like to validate questionnaire items and simulation indicators with a more representative sample of real investors, perhaps by seeking their cooperation in monitoring their real investment behaviour over a period of real months or years. A validated questionnaire and simulation would allow a potential investor to assess what his/her investment habits and preferences in a real stock market are likely to be, to compare these habits and preferences to those of other investors, and to provide feedback about their likely good or bad consequences under different market conditions. In this way, a validated questionnaire and simulation would serve as diagnostic instruments, providing comparisons and warnings to those who might not be rich enough to survive the consequences of their favoured or habitual investment strategies.
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