Long-Term Trend Analysis of Online Trading --A Stochastic Order Switching Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it