Is the unpredictability of market ups and downs a consensus in quantitative investing?
“The ups and downs of the market are unpredictable” in the field of quantitative investing is not an absolute “yes” or “no” consensus, but a “basic assumption” with strong nuances.
Simply put, the consensus in quantitative investing is: Single, specific market ups and downs (Point Prediction) are almost unpredictable, but through the law of large numbers, the probabilistic characteristics of market price distributions (Probabilistic Edge) can be predicted and profitable.
Here is a detailed analysis of the divergences and consensus in the quantitative investing community regarding “market predictability”:
Quantitative investors generally accept a practical version of the Weak Efficient Market Hypothesis (Weak EMH):
Extremely low signal-to-noise ratio: In market price fluctuations, 99% or more is “noise” (random walk), with only a tiny portion containing valid information (signal).
No crystal ball exists: Almost no serious quantitative institutions attempt precise point predictions like “What will the S&P 500 be next month?”
Rejecting “market timing”: The vast majority of fundamental quant (e.g., AQR) or statistical arbitrage strategies do not rely on predicting overall market ups and downs to make money, but instead use long-short hedging to strip out market risk (Beta), earning returns from relative strength/weakness of individual stocks (Alpha).
Famous quote: Quantitative king and Renaissance Technologies founder Jim Simons once said: “One can predict the course of a comet more easily than one can predict the course of Citigroup's stock.” This directly highlights the extreme difficulty of predicting individual asset prices.
While acknowledging “unpredictability” as the norm, the existence of quantitative investing itself is built on the premise that “the market is not completely random”. Their “prediction” differs fundamentally from what retail investors understand as “prediction”:
Dimension Retail/Traditional Perspective “Prediction” Quantitative Perspective “Prediction”
Target Guess right whether it goes up or down next time Estimate if the probability of upside is 51% or 49%
Frequency Low frequency, heavy bets on a few big opportunities High frequency, accumulating micro-profits over thousands of trades
Nature Deterministic thinking (black or white) Probabilistic thinking (distributions and expectations)
Analogy “I might guess the coin flip right”“I have a coin with 50.5% heads probability, and I’ll flip it 10,000 times”
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Different quantitative strategies rely on “predictability” to varying degrees:
High-Frequency Trading (HFT): They hardly predict future prices at all, but predict microstructure and order flow. For example, they arbitrage millisecond delays between exchanges. For them, it’s not “betting on ups and downs,” but a “head start” in physical speed.
Statistical Arbitrage (Stat Arb): They believe in mean reversion. That is: I don’t predict if the stock price will rise tomorrow, but I predict “if Stock A deviates too much from its historical correlation with Stock B, they will eventually converge.” This is a prediction of spreads, not simple ups and downs.
Trend Following (CTA): They completely abandon prediction. The core logic of CTA strategies is: “I don’t predict when trends start or end; I just follow them when they appear until they reverse.” They profit from market irrational exuberance or panic, not from predicting the future.
Cliff Asness (AQR founder): He is a proponent of efficient market theory but points out that due to retail investor herding (e.g., Meme stocks), the market may become “less efficient” in the short term, but this doesn’t mean easier to predict—instead, more noise and higher risk.
Man Group: Explicitly states that since the future cannot be foreseen, the core of quant strategies must be risk control and diversification, not betting on a single direction.
The true meaning of “market ups and downs are unpredictable” in the quant circle is:
Don’t try to play God to foresee the future; instead, like a casino dealer, use mathematical models to find those slight opportunities with win rates slightly above 50%, strictly control risk, and let probability be on your side.
For individual investors, this is indeed a consensus warning: If even quant funds with top mathematicians and supercomputers only dare to pursue 51% win rates, ordinary investors trying to get rich by “predicting ups and downs” is like climbing a tree to catch a fish.
One step I can take for you: If you’re interested in this mindset, I can explain in detail how the Kelly Criterion is used in quantitative investing to decide “since predictions aren’t accurate, how much to bet each time.”
Can different quantitative strategies rely on mutually conflicting assumptions?
This is a very profound question. The answer is: Yes, absolutely, and this is exactly the “holy grail” pursued by multi-strategy hedge funds.
In quantitative investing, different strategies not only can rely on mutually conflicting assumptions, but excellent portfolio managers will deliberately seek strategies with mutually exclusive assumptions, because this brings low correlation or even negative correlation, greatly improving the portfolio’s Sharpe Ratio.
Here are several classic “mutually exclusive assumption” cases and why they can coexist:
This is the most classic pair of “arch-enemies,” with completely opposite underlying logics:
Momentum strategy assumption: “The strong stay strong, the weak stay weak.” If an asset has been rising for a period, assume it will continue rising by inertia.
Behavioral logic: Chasing rises and killing falls.
Reversion strategy assumption: “Things reverse at extremes.” If an asset price deviates from the mean (risen too much or fallen too much), assume it will revert to the mean due to gravity.
Behavioral logic: Sell high buy low (bottom fishing, top escaping).
Conflict point: When a stock rises rapidly, momentum models signal “buy,” while reversion models may signal “short.” Why coexist:
Different time cycles: Mean reversion is usually effective in very short cycles (minutes/hours) or very long cycles (3-5 years); momentum is strongest in medium-frequency cycles (3-12 months). Quant funds can do reversion at minute level and momentum at monthly level, without interference.
Hedging risk: During big trends, reversion strategies lose money, momentum strategies make big gains; during choppy markets, momentum gets “slapped back and forth,” reversion prints money like crazy. Combined, the equity curve becomes very smooth.
In fundamental quant, this contradiction is particularly evident:
Value factor assumption: The market makes mistakes, undervaluing bad stocks. So buy cheap ones (low P/E, low P/B), usually recent underperformers.
Growth/Momentum factor assumption: The market is efficient; expensive stocks are expensive because they’re good. So buy high performers, usually higher valuations.
Conflict point: Value makes you buy “gold in the trash heap,” growth makes you buy “star surrounded by crowds.” Why coexist: Perfect complement. Giants like AQR found that value and momentum factors have historically shown slight negative correlation. Combining them reduces volatility significantly without lowering expected returns.
Short volatility assumption: Markets are calm most of the time, panic is temporary. Thus, selling options (insurance) earns premiums (Carry).
Mindset: “Peaceful times.”
Trend following (CTA) assumption: Markets will have extreme black swan events, and volatility clusters. Thus, hold options or follow trends, waiting for big volatility spikes.
Mindset: “Hoping for chaos.”
Conflict point: One bets market doesn’t move, one bets it does. Why coexist: Short vol is like “picking up pennies,” high win rate but small profits per trade (with blowup risk); CTA is like “buying lottery tickets,” small losses usually, massive gains in crises (e.g., 2008, 2020 pandemic), offsetting other strategy losses.
If the market is an elephant, different quant strategies are blind men touching it:
The one touching the leg says: “The market is as steady as a pillar (mean reversion).”
The one touching the trunk says: “The market moves flexibly like a snake (momentum).”
True quant masters (Portfolio Managers) won’t argue who’s right, because they know everyone is partial. They hire both groups, profiting no matter which part of the elephant is touched.
Conclusion: Conflicting assumptions are not only allowed but a necessary condition for building high-Sharpe portfolios. If all strategies share the same assumption and it’s wrong, the whole fund goes bankrupt.
Then they can't all profit, right?
Your intuition is spot on! If two strategies at the same time, on the same stock, do completely opposite trades, and hold for exactly the same duration, then indeed: one must be losing money (ignoring fees, it’s even zero-sum).
But in actual quant fund operations, they can profit simultaneously (or at least overall profit) due to three key “misalignment” mechanisms:
This is the biggest magic for turning conflicting strategies into allies. Market paths aren’t straight lines but winding mountain roads.
Scenario: A stock rising from 100 to 110 yuan, with a dip to 105 midway.
Trend strategy (long): Sees the big picture. Buys at 100, holds to sell at 110. (Profits 10 yuan)
Reversion strategy (short): Sees short-term fluctuations. When it surges to 108, judges a pullback, shorts, covers at 105. (Profits 3 yuan)
Result: In the same uptrend, longs profit from the big move, shorts from the interim dip. Though directions oppose, due to different holding periods, both profit.
If two strategies really want to buy 100 shares of Maotai and sell 100 shares at the same second, what happens?
Retail view: Account A profits, B loses, fund manager busy for nothing.
Fund view: Trading system detects and “internally matches.” Fund doesn’t trade a single share externally, just transfers on books.
Result: No “trading profit,” but saves massive fees and market impact costs. In quant, saved money is earned money.
Only if Strategy A wants 500 shares buy and B 100 sell, fund buys net 400 externally. This ensures conflicts don’t double-lose, only reduce risk.
Strategy profits aren’t per trade but annual expected value.
First half: Bull market.
Momentum (chasing up):** Big profits.
Reversion (bottom fishing):** Small losses (selling rallies or shorting against trend stopped out).
Total: Profit.
Second half: Choppy monkey market (high volatility, no direction).
Momentum: Drawdown (buy and drop, sell and rise, slapped back and forth).
Reversion: Huge profits (sell high buy low thrives).
Total: Profit.
Key: Since we can’t predict bull or choppy, run both. As long as profits big when winning, losses cut small, combined yearly curve is beautiful.
You’re right, in every specific conflict moment, one is right, one wrong.
But quant investing is a portfolio game. Like a soccer team:
Forwards (offensive strategies) score, defenders (defensive strategies) seem useless.
Defenders save the day, forwards no goals.
But in their specialized “match segments”, the team (fund) wins.
Thus, quant funds fear not “strategy conflict” but “strategy synchronization”—all buy or all sell signals at once means diversification fails, risk amplifies infinitely.
Explain the weak form efficient market hypothesis in detail
Weak Form Efficient Market Hypothesis (Weak EMH) is the most foundational—and most easily misunderstood—level of the cornerstone of modern finance: the Efficient Market Hypothesis (EMH).
Proposed by Nobel laureate Eugene Fama in 1970. To fully understand it, we break it down into definition, implications, impact on trading, and real-world paradoxes.
Definition: In a weak form efficient market, current asset prices fully reflect all past market transaction information.
“Past market transaction information” here specifically means:
Historical prices (Historical Prices)
Historical trading volume (Trading Volume)
Historical returns (Past Returns)
Plain explanation: Everyone stares at past K-line charts; if there’s truly a “sure-profit pattern” hidden, smart traders would’ve found it long ago. They arbitrage it away instantly, adjusting prices. Thus, any historical patterns you see are useless for predicting future prices.
This is where weak EMH “offends” most. If true:
All K-line patterns (e.g., “three black crows,” “head and shoulders,” “golden/death cross”).
All technical indicators (MACD, RSI, Bollinger Bands).
Conclusion: Chart trading is futile. You can’t gain long-term alpha from past charts.
Today’s price change unrelated to yesterday’s (no serial correlation).
Like coin flips: 10 heads in a row, 11th still 50% heads. Stock rose yesterday doesn’t mean more likely up (no momentum) or down (no reversion) today.
Your prior questions mentioned “momentum” and “reversion,” both based on historical prices.
Momentum: Past rise means future rise.
Reversion: Past rise too much means future fall.
Huge paradox: If weak EMH holds, past doesn’t affect future, so momentum and reversion should both lose money. But in reality, they’re top quant profit sources.
Academic and industry explanation: Proves market isn’t perfectly weak efficient. Quant profits are from market inefficiency.
In today’s investment world, attitudes toward weak efficiency are nuanced:
For average investors, buying on MACD golden cross long-term loses. Simple patterns mined dry by thousands of algos. For retail, market is weak efficient, even stronger.
Quant work is finding statistically slight inefficiencies.
Not simple “golden cross,” but deep learning for nonlinear, ultra-complex historical patterns.
Simple K-lines useless, but combining microstructure (Order Flow), vol surfaces, etc., historical data still hides faint signals.
Weak EMH is like “feather and iron ball fall together in vacuum” in physics:
Theoretically: Finance benchmark, warns against driving by “rearview mirror” (don’t worship TA).
Reality: Air resistance (human irrationality, frictions) exists.
Conclusion: Market approaches weak efficiency, but not perfectly. Quant traders use microscopes to find “air resistance” and profit.
Is the rise and fall of a single stock unpredictable? Are there no high win-rate methods?
This is a soul-striking question. Straight answer:
For single stocks, “high win-rate” methods exist, but they come with two fatal costs: either “extremely low returns” or “extremely high risk (total wipeout once).”
If your goal is “high win rate + high returns + low risk” for predicting single stocks, answer: No. If exists, it’s insider trading (illegal) or Ponzi scheme.
Break down “single stock prediction” into harsh realities:
You can build 90% or even 99% win-rate strategies for single stocks, but it’s poison.
Method: Stock at 100 yuan. Predict “tomorrow won’t drop over 10%.”
Trade: Sell deep OTM puts or ultra-short grid trading.
Result: 99% days no 10% crash, earn daily “premiums” or “micro-profits.” Win rate near 100%.
Cost: That 1% day (earnings blowup, regulatory black swan), drops 20%. Wipe out 3 years’ gains in one day, even owe broker.
Conclusion: High win rate on single stock often trades 扛单 (unlimited tail risk) for fake win rate.
Quant institutions avoid heavy single-stock bets due to low signal-to-noise.
Individual randomness: Predict “national car crash deaths next year” accurately (law of large numbers), but not “if neighbor Wang crashes tomorrow.”
Black swans dense: Unique risks (Idiosyncratic Risk). CEO quits, factory fire, client cuts—discrete, unmodelable.
Whale games: Small capital: random walk victim; large: prey.
Though ordinary can’t predict, in ultra-short or ultra-specific dimensions, single stock moves predictable:
HFT: Not “tomorrow up/down,” but “next 10ms, more buys or sells?”
Logic: Order book imbalance, next 1s up prob 80%.
Barrier: Billions in gear, co-lo servers, top algos.
Merger arb: A announces 50 yuan buy B at 48.
Logic: Unless fails, rises to 50. High win rate.
Risk: Antitrust kill, drops to 30.
If must single stock, quant advice: Abandon point prediction, shift to conditional probability:
Don’t ask: “Will Maotai rise tomorrow?” (Coin flip, 50%).
Ask: “If Maotai breaks yearly line, volume 3x, liquor sector down, prob continues down 3 days?”
Quant essence: Seek “conditional bias.” Even with conditions, win rate maybe 55% or 60%. Top level.
Single stock rise/fall: short-term Brownian motion (random), long-term fundamental mapping.
“High win-rate” usually two types:
You’re whale/insider (see cards).
“High prob small win, low prob big loss” (survivorship bias, high win if no blowup).
Rational for individuals: Not seek “sure-win prediction,” admit “don’t know next,” use position mgmt (stops, sizing) to survive wrongs.
One step: Win rate vs payoff imbalance causes losses: “high win low profit, low win big loss.” Want Risk/Reward Ratio calc, profit at 40% win?
Under what background was the weak form market hypothesis proposed, and what problem does it explain?
Weak Form EMH’s proposal marked finance shifting from “sorcerer mysticism” to “serious science.”
Understand via mid-20th century: computers newborn, Wall Street hand-drawing K-lines.
Pre-1950s-60s, no solid theory; two camps:
Chartists: TA ancestors. Stocks like tides/weather, fixed cycles/waves. Find rule, predict.
Fundamentals: Value by company, but no data tools.
Key Turns:
Kendall’s “Failure” Shock (1953): UK statistician Maurice Kendall analyzed UK stocks for cycles. Result: No patterns! Like drunkard’s walk. Wrote: “Data like wandering drunk, no direction.”
Computers in Finance (1960s): Fama et al. used big data. Complex stats couldn’t predict future from past prices.
Fama’s Summary (1970): Milestone paper “Efficient Capital Markets: A Review,” defined “weak form” baseline.
Weak EMH explains three puzzling phenomena:
Direct target.
Issue: Everyone knows K-patterns, why few riches?
Explain: In info-flow market, historical prices cheapest.
“Down 3 days, up day 4” true? All buy day 3 close, lift price, profit arbitraged day 3.
Conclusion: Past-price rules, once found, invalidated by arb.
Kendall’s random walk panicked: casino? Weak EMH brilliant counter:
Issue: Why random?
Explain: Market too smart.
Price reacts info.
Future news random.
Fast reaction: yesterday info in yesterday price; today change only today’s news.
Conclusion: Randomness reflects new info randomness, not irrational market.
Breaks illusion: simple effort (stare screens) beats market.
Issue: Why pros lag index?
Explain: “Historical moves” no future info. Weak market: next move independent (coin: 10 heads, next 50%).
Background: Post-computer analysis found no stock cycle rules.
Problem Solved: Theoretically “death sentence” for pure TA. Tells world: Don’t seek future in rearview. Beat market? Dig beyond public info (leads to semi-strong/strong EMH).
Like magic reveal: Know trick (historical rules), no more magic (alpha), can’t fool.
But now quants earn excess returns too,
Very sharp counter!
If weak EMH says “historical data useless,” why do Renaissance, Citadel quant giants rake it in?
Not EMH bankruptcy, but theory-reality chemistry. Quants earn alpha via:
Weak EMH: Only historical prices/vol no profit. Today’s quants beyond:
Earn from “info/calc power gaps.”
Classic answer by Nobelists.
Weak EMH kills simple linear like “MACD cross up.”
Complex nonlinear: exist, but:
Undetectable sans DL/HFT.
Seem “steady alpha” often 隐形保险.
EMH not wrong, “absolute vacuum”.
Reality has drag/fragments. Quants use microscopes (compute)/tweezers (algos) to grab crumbs in ms/days before full reflection.
But there is also long-term quant
Your rebuttal precise! Beyond “fast in/out,” you see quant’s other mountain: long-term quant (fundamental quant / factor investing / Smart Beta).
E.g., AQR, DFA, China index enhancers: holds months/years, not ms/days.
Not “speed wins,” what do they earn long-term? Modern finance alternative alpha explanation:
Finance gem: EMH proposer Fama made Fama-French Three-Factor Model, ancestor of long-term quants.
Long quant: market efficient, but risks beyond market.
Conclusion: Much alpha “hard labor fee”—bear risks retail/public shun.
Humans greedy/fearful, biases unchanged centuries. Machines harvest structural mispricings:
Subjective long like Buffett: 20 stocks, godlike insight.
Long quant (index enhance):
Quant breadth. No single predict, massive stats via LLN.
HFT assassin “speed unbeatable”; long quant “farming”:
Not defy “hard predict.” No “tomorrow who,” but: “Spring: Sow wide quality/cheap/high-div seeds. Birds/drought some, but harvest > weeds (plain index).”
Long quant logic surviving/profitting in weak EMH.