Chess of Stocks: How Does the Trader Calculate His Moves Accurately?

A world chess player does not start his calculations from scratch with every move his opponent makes on the board; he does not completely re-study his plan or ignore the opening and previous maneuvers, but rather treats the new move as an 'emerging development' that updates his overall vision of the match, without changing it entirely.

With this same mindset that works on 'updating the picture' instead of changing it, the logic of 'Bayesian Analysis' in the stock market is founded; this model rejects the idea of stagnation or processing events in isolation from their context or the overall picture.

The Intersection of Old and New

The basic idea here relies on the principle of 'data frequency and integration,' meaning that the investor does not need to build his hypothesis from scratch with every piece of information that appears about a stock, but rather relies on his prior information background and updates it to produce a more recent financial reading, which may prompt him to move in the market (buying or selling), or he may decide to remain as is, outside or inside the market.

This concept is clearly evident when monitoring companies' 'quarterly earnings reports'; if prior expectations indicated that the probability of a stock rising was 50%, and then the company surprised everyone by announcing huge operating profits 'above expectations,' Bayesian analysis incorporates this development (unexpected profits) to raise the probability of an uptick to 70%, for instance.

Conversely, if the results are disappointing and 'below expectations,' the equation automatically reverses, greatly reducing the probability of a rise, forcing the investor to immediately reduce his relative weight in the stock to manage risk (in the case of short-term investing).

The essence of Bayesian analysis is to rearrange information based on the intersection of old and new numbers, and it has a simple formula:

(New Probability) = [(Probability of new evidence occurring if stock rises) × (Old probability of stock rising)] ÷ (General probability of this new evidence occurring in the market).

Suppose an investor manages a growth stock portfolio and holds a financial position in a promising tech company, and his prior estimates indicated that the probability of the stock rising by 20% was 50%.

Later, the company announced operational contracts and profits exceeding expectations (this is the new evidence that enters Bayesian analysis). Based on a historical review of this stock's trading, it becomes clear that historically, the appearance of above-expected profits raises the stock's rise rate to 80% in the period following those earnings or cash flows.

If the general probability of the company reporting above-expected profits in any normal circumstance is 55%, then applying the formula gives the updated probability of a rise = (0.80 × 0.50) ÷ 0.55 = 72.7%.

With the probability of the stock price increase rising from 50% to 72.7%, this may give the investor more confidence to inject additional liquidity and double the stock's relative weight in his portfolio, based on updating the overall picture according to Bayesian analysis methodology.

Pivotal Updates and Marginal Ones

For example, when analyzing a sector like artificial intelligence companies, three factors must be constantly updated in this way to ensure the investor gets an 'updated' and true picture of his investments, especially given the rapid change in this sector.

The three factors are: the size of capital expenditure, actual revenue growth rate (measuring real operating returns from selling advanced computing software), and the cash flow and liquidity burn coefficient (reflecting the ratio between capital operating costs and free cash flows).

Once these data are monitored, they are entered into the Bayesian analysis matrix to update the overall picture of the sector between two completely opposite states: the first positive, the second negative.

If the variables are moving on a healthy and sustainable financial path, and the massive expansion in capital expenditure is accompanied by frequent data confirming a parallel and documented jump in revenues and cloud subscription sales related to artificial intelligence by a large percentage, say 45%, after previously growing at lower rates, this drives the investor to hold on to his stocks in this sector or try to acquire some if he is outside those companies.

In contrast, new data may shock markets with a sharp structural financial imbalance (as is currently happening with many AI companies), where companies continue to spend billions on expanding capital expenditure, while actual revenue growth rates are disappointing and 'nearly flat' without real growth, prompting investors to get rid of those stocks.

A Volatile Picture and a Stable One

For example, Johnson & Johnson's stock is considered a defensive stock, with relatively low growth rates, but with stable dividend distribution and a field of limited volatility compared to others.

The company's historical beta coefficient (i.e., its stock volatility vs. market volatility) is only 0.55, while its annual standard deviation moves in a narrow range of about 12%, with annual free cash flows exceeding $18 billion.

Thus, the stock is not greatly affected by Bayesian analysis; whether in times of volatility or recovery, the impact of changing numbers on the overall picture of the stock remains limited, meaning the effect of a single number on the picture in a way that justifies the investor's market move remains limited.

In contrast, NVIDIA emerges as one of the companies most affected by the AI boom, if not the most, so NVIDIA's stock records a high beta coefficient fluctuating around 1.75, while its annual standard deviation jumps to sharp levels exceeding 40%, despite its acceptable debt ratio and free cash flows that leaped to exceed $26 billion thanks to demand surge.

Here, Bayesian analysis becomes extremely vital; this stock cannot be evaluated using old data, as every quarterly report containing an update to future revenue guidance must be treated as an important indicator that needs immediate attention, due to its clear impact on the company's future as a whole, and its chances of thriving or not, not only in the short term.

Successful with Bayesian Analysis

The man known as the 'godfather of quantitative investing,' Edward Thorp, provides evidence of the efficacy of the Bayesian analysis method in practice; Thorp, whose fund achieved extremely high returns, did not record a single quarterly loss.

Thorp believes that one should always monitor and evaluate the new development, but without re-studying all the details related to the companies every time, so the investor must be aware of the strengths and weaknesses of the company he invests in, making 'updating the picture' easier and decision-making smoother.

Based on this approach, Thorp achieved from his personal investments a compound annual average return of approximately 20% over more than 28 consecutive years of trading, while two investment funds he established generated profits ranging between 15.1% and 18.2%.

According to what Thorp says, for example, cash flows should be the key marker in updating the picture of AI companies, while changes in debt are most important for a company like Boeing, which suffers from large debts (in addition to the sensitivity of the aviation sector to many variables in general).