Long-Term Trend Analysis of Online Trading --A Stochastic Order Switching Model
Bibliographic record
Abstract
Online brokerages are replacing brokers and telephones with computers and codes, and compete intensely for investors. The investment costs for setting up an online service are far lower than starting a traditional full-service brokerage. Attracted by the low commissions and high convenience of online trading, there has been an explosion in online trading that is likely to continue in the next decade. There are many advantages and disadvantages to online trading. In this research, we study the long-term trend of investors' orders submitted to two types of brokerages: e- and non-e-brokerages in the stock market. To understand how investors choose trading channels, we identify five important factors that affect the investors' choice of brokerages. Since some factors are qualitative, we develop linear formulas to convert multiple factors and imbedded multiple attributes into scalars to measure investors' overall preferences of brokerages. Based on the investors' preference measures of brokerages, a stochastic process called the order-switching model is then developed to study the impact of investors' preferences on the number of orders submitted to each type of brokerage in the stock market. Both analytical and empirical results are derived and provide many insightful observations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".