The same lecture notes teach technical analysis and why it fades

Yes — at least some universities teach technical analysis formally, not as a curiosity and not only as a footnote to the efficient markets chapter. There are full courses, with lecture notes you can open.

⚠️ Read that at its actual strength. One open university course is not evidence that universities generally endorse technical analysis. It is evidence that the subject has a formal place in at least some university finance curricula, which is a smaller and more checkable claim.

And the interesting part is not that such courses exist. It is what they contain: the same document that teaches the method also lists the reason a working rule stops working.

Do professionals actually use it?#

Start here, because it is the fact that makes the rest worth explaining. In two well-known surveys — of professional foreign exchange dealers and of fund managers — technical analysis was in heavy use. ⚠️ Those are specific populations at specific dates, not a global census of finance professionals, and every figure below should be read with its population attached.

Taylor and Allen (1992) ran a questionnaire on behalf of the Bank of England among chief foreign exchange dealers in London in November 1988. At least 90% placed some weight on technical analysis at one or more time horizons. At horizons of a week or less, 90% reported using some chartist input, and 60% regarded it as at least as important as economic fundamentals.

Menkhoff (2010) surveyed 692 fund managers across the US, Germany, Switzerland, Italy and Thailand. The vast majority rely on technical analysis, and the finding worth quoting exactly is about horizon:

At a forecasting horizon of weeks, technical analysis is the most important form of analysis and up to this horizon it is thus more important than fundamental analysis.

He also reports that the technicians in his sample were as experienced, as educated and as successful in their careers as everyone else. Whatever is going on, it is not a story about unqualified people.

So what does a university course actually teach?#

Take a concrete one. NPTEL — the open-courseware programme run by India's IITs — carries Security Analysis and Portfolio Management from IIT Kharagpur, coordinated by Dr. Jitendra Mahakud. Technical analysis is Module 10, Sessions 19 and 20, sitting inside a full syllabus that runs from market efficiency and financial statement analysis through to portfolio theory, CAPM and derivatives.

The notes do not hedge about what the subject is. They open with the International Federation of Technical Analysts' definition — the systematic analysis of financial instruments using only market-delivered information such as price, volume, volatility and open interest — and then set out four underlying assumptions, of which the load-bearing one is that prices tend to move in trends that persist for appreciable lengths of time.

Then the indicators, the trading rules, and the comparison with fundamental analysis. It is a real treatment of the subject, not a dismissal.

⚠️ It is also one course, and we are not claiming it represents every finance faculty. What it does establish is the thing people actually want to know: a formal, examinable university treatment of technical analysis exists, and you can read it. Others do too — Mehmet Dicle, Associate Professor of Finance at Loyola University New Orleans, teaches a technical analysis course and publishes open lecture notes on it that connect the subject to behavioural finance. Which, as it happens, is where Menkhoff's survey also lands: the strongest correlate of using technical analysis was the view that prices are heavily determined by psychological influences.

What the same notes say about it#

Four sections later, under Challenges to Technical Analysis, the same document lists the case against — starting with the observation that empirical tests of the efficient market hypothesis show prices do not move in trends, which is the first of its own four assumptions.

Then, among the challenges to trading rules themselves:

A successful rule will gain followers and become less successful.

That is ten words in a lecture note, and it is the most important sentence on this page.

The line and the measurement are the same claim#

Read that sentence next to what happened when someone checked.

Brock, Lakonishok and LeBaron found in 1992 that simple moving-average and breakout rules worked on the Dow from 1897 to 1986. Sullivan, Timmermann and White applied a data-snooping correction across roughly 8,000 rules and confirmed the finding survived. Then they looked at the ten years nobody had seen — 1987 to 1996 — and the best rule selected on the earlier data returned 2.8% a year at a p-value of 0.32.

And the explanation the authors themselves offered was market efficiency improving: cheaper computing power, lower transaction costs, more liquidity. Which is the lecture note's sentence, written as a finding rather than as a caution. We laid out that whole arc in the reading list.

Two independent routes — one from teaching the subject, one from testing it — arrive at the same mechanism. A rule can lose its economic value as markets adapt to it, which is precisely the decay the lecture notes warn about. ⚠️ That is not a law that every profitable rule is consumed by adoption; it is one well-evidenced way a published rule can stop paying, and the authors of the out-of-sample study offered it as their own explanation rather than as a proof.

That reconciles the three facts this article started with. Professionals use it heavily. Universities teach it seriously. And the empirical record shows in-sample results that do not persist. None of those three has to be wrong for the other two to be true.

What to do with this if you are learning#

Three things follow, and none of them is "stop reading about technical analysis".

Prefer the course to the course-shaped product. A syllabus that puts technical analysis after market efficiency and before portfolio theory is telling you where it sits in a larger structure. That framing is most of the value, and it is exactly what a standalone indicator tutorial removes.

Read the challenges section. Every serious treatment has one. If a course, book or video has no section on when the method fails, you are not looking at a treatment of the subject — you are looking at an advertisement for it.

Do not read a decay mechanism as a reason to skip the subject. Ninety percent of a professional population is not using a tool for no reason, and "this specific published rule stopped working after it was published" is a much narrower claim than "none of this is worth understanding". The vocabulary and the chart conventions remain useful even when a specific trading rule stops working.

Where we sit, and what this does not say#

We are not the arbiter here. Our own work sits at a far shorter horizon than any of this — fifteen minutes rather than weeks — and it keeps producing the same shape: five confirmation rules that were all statistically significant on 1.13 million observations and all worth about half a percentage point; a naive contrarian rule that is a coin over 366,606 unfiltered decision points. We are a small recent instance of a long pattern, not a verdict on it.

This piece does not claim that university teaching validates technical analysis — the same notes argue against it, which is the point. It does not claim the surveys measure profitability; they measure use, and the two are different questions. It does not survey the world's finance faculties; it opens two of them. And it does not tell you what to trade.

What it does say is narrow and, we think, useful: the honest treatments of this subject — the academic ones — teach the method and its decay in the same breath, and any source that gives you only the first half has edited the second half out.

SwipeTA is a training game and a simulation: no real money, no broker, and it does not provide investment advice. The parameters behind our own measurements are on the methodology page.