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A Malaysian academic study titled “Proprietary Day Traders and Their Performance at Bursa Malaysia”, written by Saw Imm Song from Universiti Teknologi MARA (UiTM) and Ei Yet Chu from Universiti Sains Malaysia (USM).
The study focuses on licensed proprietary day traders, also known as IVT dealers, who trade using a bank’s capital on Bursa Malaysia. The study examined 122 proprietary day traders from one major Malaysian investment bank. This sample represented about 50% of all licensed proprietary traders in Malaysia at that time.
The research covered 82 active trading days, from October 2016 to January 2017. This was a weak market period, during which the KLCI fell from 1,672 to 1,619, then recovered to 1,671.
What makes this study interesting is that it used real trading data, not survey answers or personal opinions. The researchers studied actual trading performance, including daily and monthly profits, winning ratio, number of counters traded, credit limit, and traders’ backgrounds.
PART 1: WHO ARE THESE TRADERS? (Descriptive Profile)
Characteristic | Mean | Median | Std Dev | Min | Max |
| Age (years) | 32.7 | 28 | 10.8 | 22 | 69 |
| Years with firm | 3.31 | 1.67 | 3.65 | 0.08 | 16.75 |
| Credit Limit (RM’000) | 1,581.68 | 800 | 1,958.80 | 20 | 12,500 |
| Daily Profit (RM) | 691.57 | 224.06 | 1,873.04 | −8,086 | 10,903 |
| Monthly Profit (RM) | 13,303 | 3,897 | 36,531 | −171,459 | 200,195 |
| Winning Ratio | 41.4% | 40.1% | 13.0% | 20.9% | 76.8% |
| Counters traded/day | 9.81 | 8.91 | 5.84 | 1.95 | 43.2 |
Key profile message:
• 90% are male, 10% female
• Mostly young — median age just 28 years
• Mostly early-career — median tenure only 1.67 years
• 71.3% have highly relevant education (banking, finance, investment)
• 35.2% have highly relevant prior work experience
• Credit limits are highly skewed: median RM800k, but some have RM12.5 million
PART 2: THE TWO STATISTICALLY SIGNIFICANT PREDICTORS OF PROFIT
Regression Results (Table 8) — What actually predicts monthly profit?
| Predictor | Model 1 (B) | Sig. | Model 2 (B) | Sig. | Verdict |
| Constant | 23,080.70 | 0.16 | 15,398.51 | 0.34 | Not significant |
| Age | −588.13 | 0.17 | −499.54 | 0.23 | Not significant |
| Gender | 197.29 | 0.98 | 4,724.29 | 0.64 | Not significant |
| Years with firm | 4,889.34 | 0.00 ✓✓ | 2,936.93 | 0.03 ✓✓ | SIGNIFICANT |
| Relevant Experience | −10,793.87 | 0.18 | −13,146.77 | 0.10 ✓ | Weak negative |
| Relevant Education | −4,136.78 | 0.57 | −5,228.94 | 0.46 | Not significant |
| Capital Limit | — | — | 5.59 | 0.01 ✓✓✓ | SIGNIFICANT |
| R-squared | 0.15 | 0.20 | |||
| Adjusted R-squared | 0.11 | 0.16 | |||
| F-statistic | 4.10 | 4.85 | Overall model significant |
What actually predicts a trader’s profit?
The regression results point to two things, and two things only: how long a trader has been with the firm, and how much credit they’re given.
Each extra year with the firm adds RM2,937 to RM4,889 a month in profit. Each extra RM1 million in credit limit adds about RM5,590 a month.
Age, gender, and education showed no real connection to profit. Coming from a broking or banking background showed a slight negative connection (around RM13,147 a month less), though this result is weaker and less certain than the other two.
The overall model explains 20% of the difference in profit between traders — a moderate fit, leaving most of what separates a profitable trader from an unprofitable one still unexplained.
PART 3: PROFIT BY CREDIT LIMIT — Full Table Breakdown
| Credit Limit | n | % Loss-making | Most Common Profit Band | Top Earners (>RM100k) |
| <RM100k | 28 | 50% | Low (<RM2k) | 0% |
| RM101k–300k | 14 | 21.4% | RM0–5k | 0% |
| RM301k–500k | 14 | 21.4% | RM5k–10k | 0% |
| RM501k–1m | 12 | 16.7% | RM5k–20k | 0% |
| RM1m–3m | 39 | 15.4% | RM10k–20k | 0% |
| RM3m–5m | 10 | 20.0% | RM20k–100k | 40% |
| >RM5m | 5 | 20.0% | >RM40k | 0% (but 40% earn RM40–100k) |
| >RM3m top earners | 4 | 0% | >RM100k | 100% |
Half of the traders with under RM100k credit are losing money. No trader below RM1 million in credit ever reached the RM60k+ monthly profit band. And every trader earning over RM100,000 a month had a credit limit above RM3 million.
PART 4: PROFIT BY YEARS OF EXPERIENCE
Years with Firm n % Losing % Earning >RM10k/month % Earning >RM60k/month
<6 months 17 41.2% 0% 0%6 months–1 year 27 29.6% 0% 0%1–2 years 22 36.4% 18.2% 0%2–3 years 10 0% 30%+ 0%3–5 years 21 28.6% 28.6% 14.3%5–7 years 8 0% 37.5%+ 25%
Years with Firm | n | % Losing | % Earning >RM10k/month | % Earning >RM60k/month |
| <6 months | 17 | 41.2% | 0% | 0% |
| 6 months–1 year | 27 | 29.6% | 0% | 0% |
| 1–2 years | 22 | 36.4% | 18.2% | 0% |
| 2–3 years | 10 | 0% | 30%+ | 0% |
| 3–5 years | 21 | 28.6% | 28.6% | 14.3% |
| 5–7 years | 8 | 0% | 37.5%+ | 25% |
| >7 years | 17 | 11.8% | 52.9% | 29.4% |
The progression story:
• Under 6 months: 4 in 10 are losing money
• After 2 years: loss rate drops dramatically, positive returns begin dominating
• After 3–5 years: first appearance of traders earning >RM60k/month
• After 7+ years: 52.9% earn more than RM10,000/month, and 17.6% earn >RM100k/month
PART 7: PROFIT BY EDUCATION & EXPERIENCE
| Education Level | n | % Losing | % Earning >RM100k |
| Highly Relevant | 87 | 31.0% | 2.3% |
| Relevant | 5 | 0% | 0% |
| Not Relevant | 30 | 13.3% | 6.7% |
• Surprisingly, traders with no relevant education have a lower loss rate (13.3%) than highly relevant educated traders (31%)
• 50% of top earners (>RM100k) come from non-relevant education backgrounds
• Education is NOT statistically significant — the raw pattern is explained by other variables
| Experience Level | n | % Losing | % Earning >RM60k |
| Highly Relevant | 43 | 27.9% | 11.7% |
| Relevant | 24 | 29.2% | 12.5% |
| Not Relevant | 55 | 21.8% | 5.4% |
Prior work experience shows no clear pattern — loss rates are similar across all groups
The regression confirms experience is weakly negative at best (p=0.10)
PART 8: WHO IS PREDICTED TO BE A SUCCESSFUL TRADER
Based on the statistically significant regression findings, here is the evidence-based success profile:
The Two Key Factors That Truly Influence Success
SUCCESS = f (Years of Tenure) + f (Capital Limit)
PART 9: HOW MUCH CAN A TRADER EXPECT TO MAKE EACH MONTH?
| Trader Profile | Years | Credit Limit | Expected Monthly Profit |
| Brand new, RM100k credit | 0.5 | RM100,000 | ≈ RM1,469 |
| 1 year, RM300k credit | 1 | RM300,000 | ≈ RM4,611 |
| 2 years, RM500k credit | 2 | RM500,000 | ≈ RM9,669 |
| 3 years, RM1m credit | 3 | RM1,000,000 | ≈ RM19,500 |
| 5 years, RM2m credit | 5 | RM2,000,000 | ≈ RM41,974 |
| 7 years, RM3m credit | 7 | RM3,000,000 | ≈ RM56,168 |
| 10 years, RM5m credit | 10 | RM5,000,000 | ≈ RM82,770 |
| Top performer (>7yrs, RM12.5m) | 10+ | RM12,500,000 | ≈ RM154,000+ |
This data paints a clear picture of day trading on Bursa Malaysia. Profit is not down to luck, age, gender, or education. It comes down to two things: how long a trader stays with the firm, and how much capital the firm puts behind them.
A quarter of all traders lose money over the study period, with losses concentrated in the first six months. The picture turns past two years, and the highest earners combine years on the desk with credit limits running into the millions of ringgit.
For a retail investor reading this from outside, the takeaway is plain: day trading on Bursa Malaysia can pay, even in a weak market, but the path to consistent profit runs through years of seasoning and capital backing that most independent retail traders won’t have access to. Without that kind of institutional support, the climb to similar results is much steeper.
Reference
Song, S. I., & Chu, E. Y. (n.d.). Proprietary day traders and their performance at Bursa Malaysia (pp. 225–242).