How effective can 'machine learning' be for 'stock price prediction'?

We randomly selected four stocks from different trading markets: one from the Shanghai Stock Exchange, and others from the SME Board and ChiNext of the Shenzhen Stock Exchange—namely, Agricultural Products (000061), Hua Tian Technology (002185), Zuoli Pharmaceutical (300181), and Shanghai International Port Group (600018). The yellow curve in the chart represents the predicted daily percentage change in stock price, while the blue curve shows the actual daily percentage change observed for each stock. All data spans from November 1, 2024, to December 31, 2024. Note that the date labels on the horizontal axis are incorrect—please disregard them for now; they will be corrected in the next software update. The yellow curve forecasts the price change for the *next* trading day. Therefore, if a yellow peak (or trough) appears *before* the corresponding blue peak (or trough), the prediction is considered correct. For example, if the yellow curve shows a peak—indicating the AI predicts an upward price movement on the next trading day—and a blue peak indeed follows immediately afterward—confirming an actual price increase on that next day—then this prediction is deemed successful. Otherwise, it counts as a failure. We are sharing several charts today solely to demonstrate that the fundamental idea of using AI for stock prediction is feasible, and that the resulting forecasts hold some practical reference value. Currently, only the simplest machine learning program has been employed; many details still require further refinement, and numerous alternative training methods remain untested. All programs were written in Python, with fewer than 200 lines of code across just two source files. Such simplicity yielding such striking results exemplifies the magic of artificial intelligence—this is merely one tiny facet of AI’s vast potential. All source code has been open-sourced and published on a domestic open-source platform. The link is as follows: gitee.com/errnet/stock Interested readers may download and run the code, modify it as desired, build models using their preferred stocks, train those models, and then adjust the prediction timeframe to forecast the latest stock data.

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Sales velocity (‘dongxiao’): Through sampling observations at several retail outlets, the shelf age of RIO pre-mixed cocktails currently available on the market ranges predominantly between 2–5 months, indicating robust sales velocity and virtually no inventory overstocking. In contrast, the competing product ‘Helayi’, launched by Japan’s Suntory Holdings, generally bears earlier production dates and thus longer shelf ages, rendering its competitiveness comparatively weaker. Furthermore, the survey reveals that sales velocity of pre-mixed cocktails is influenced more significantly by distribution channel than by geographic region. For instance, in first-tier cities like Shanghai, RIO products with shelf ages up to 10 months can still be found even in small convenience stores; meanwhile, in relatively remote regions such as Xinjiang and Guizhou, channels carrying RIO products with shelf ages of only 2–5 months are widespread. (Source: Future Think Tank) 4. Profitability Analysis 4.1 Key Assumptions Pre-mixed cocktail business: Given the company’s ample production capacity—with current utilization at approximately 30%—sales volume forecasts are not constrained by capacity shortages. Moreover, the company adopts a ‘produce-to-order’ strategy for its pre-mixed cocktail production planning, achieving high production-sales alignment. Consequently, revenue from this business segment primarily hinges on sales performance, analyzed here through two dimensions: sales volume and unit selling price. Sales volume: In 2021, given that pre-mixed cocktail sales volume reached 11.3895 million cases in H1 2021—a 56.44% year-on-year increase—and quarterly year-on-year revenue growth rates for Jan–Sep 2021 stood at 52.9%, 54.4%, and 31%, respectively, we project full-year 2021 sales volume growth at 45%. We further forecast year-on-year growth rates of 30% and 25% for 2022 and 2023, respectively. Per-case price: From 2016 to 2020, the calculated per-case price (derived as pre-mixed cocktail revenue divided by case volume sold) was RMB 114, 112, 106, 101, and 95, respectively. We therefore anticipate continued downward pressure on the 2021 per-case price. However, according to disclosed unit prices for the company’s three primary packaging formats—275mL cans, 330mL cans, and 500mL cans—from 2018 to 2020 and into 2021, actual unit prices showed no significant decline. This suggests that the observed per-case price reduction stems mainly from shifts in the sales mix across packaging formats, and from the gradual replacement of bottled products with canned ones—both of which lower average per-case pricing. Based on semi-annual report data, the estimated per-case price for H1 2021 was approximately RMB 92.76. Accordingly, we set the full-year 2021 per-case price at RMB 92.76. Considering diminishing scope for further price reductions via product-mix adjustments, we assume per-case prices of RMB 90 and RMB 89 for 2022 and 2023, respectively. Additionally, factoring in the company’s announced price increases of 4–10% across products, we adopt an average increase of 7%, leading to projected per-case prices of RMB 96.3 and RMB 95.23 for 2022 and 2023, respectively. Costs: Based on semi-annual report data, the estimated per-case cost stands at approximately RMB 30.26. Accounting for anticipated price hikes in raw materials and packaging in H2, we estimate the full-year 2021 per-case cost at RMB 31.47.

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