Questions tagged [prediction]

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15 views

How could house prices tend to rise in the long-term? [migrated]

It makes sense that house prices increase above the risk-free rate due to their risky nature and historical evidence and conventional wisdom seems to back up the idea that real house prices tend to ...
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0answers
34 views

Presence of underestimation bias in consensus earnings predictions

I am working on a financial data that entails forecasted revenue a company generates over a fiscal quarter and the actual revenue for that quarter. ...
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2answers
2k views

Tools/R code for predicting Dragon-Kings

The theory of the so called Dragon-Kings, esp. by Didier Sornette (ETH Zürich), basically states that financial crises and crashes are predictable (contrary to the theory of black swans). The ...
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1answer
189 views

Proper Data Partitioning For Building a Forecasting Model

Goal: A team and I are looking to build a model that performs a predictive action for the state of the market on day T + n, using the data at hand on day ...
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1answer
82 views

Book/Material recommendation - Stock Price Forecasting using AI tools

I am looking for a book, which covers the following topics: stock price prediction using Artificial Neural Network, stock price prediction using LSTM, stock price prediction using linear/non-linear ...
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0answers
37 views

Machine learning in stock price prediction [duplicate]

I am new and thinking to experiment in the stock price predication. There are many way like moving average but I am interested in using machine learning. Anyone can help me here to give pointer?
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83 views

stock price trend classification using Random Forest in sklearn

I have created a random forest classification model in skicit-learn, but I am unsure how to finalise my forecast. I have built the model and it is showing good results on the testing data. I get a ...
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1answer
85 views

Prediciting outperformance - choice of statistical design?

I want to predict relative outperformance between a stock and an associated benchmark index using statistical time-series models (e.g. ARIMA) and some exogenous variables (day of the week, corporate ...
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0answers
155 views

Current research in price prediction

I am returning to studying the markets after ten years spent in banking. I would like to ask for directions, what approaches to price prediction are currently used - I want to catch up quickly. Papers ...
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1answer
46 views

Find best linear predictor of $X_2$ given $1, X_1$

I'm having a problem calculating the best linear predictor of a time series. I'm using the book Brockwell-Davis 2016 - Introduction to Time Series and Forecasts. First let me write down one notational ...
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0answers
50 views

Why is the approximate entropy of (some) stock returns zero?

I downloaded some prices for TSLA and AMZN from yahoo finance to try and see if I could measure the entropy on a rolling basis with the intention being maybe returns have lower entropy (are more ...
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0answers
40 views

Predicting smoothed returns

Due to the extremly low ratio of signal to noise in financial data, predicting raw returns is very difficult. If we smooth out the price time series, say by an EWMA, and then calculate returns on this ...
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0answers
65 views

Normalization of cryptocurrency / stock price data

I am making predictions on cryptocurrency / stock price data. I am using the candlestick data (High, Low, Open, Close) to estimate the close price of the next day. I am looking for the right way to ...
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1answer
52 views

Non-fixed stationary "conversion"

Dear users of StackExchange, I was wondering why the log returns of a fixed period of time is such a common use in "transforming" a time series into a more stationary one? I thought that ...
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1answer
87 views

Using candlesticks for Stock price direction prediction

I am working on a college project wherein I want my machine learning model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as ...
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0answers
50 views

How to use machine learning to generate optimal allocations for an instrument?

What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available ...
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5answers
398 views

Technical Indicators reference

I have been looking for a good reference where I can find how technical indicators of stock market analysis are calculated. I have a dataset (time series) which I want to extract these indicators to ...
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0answers
52 views

Why are prediction markets based on logarithms when a linear solution can suffice?

For example, take a binary outcome; A coin toss, heads or tails. If heads, then those that picked heads receive \$1 and tails receive \$0. To quote the prices for each bet Hanson's LMSR uses ...
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1answer
53 views

Relationship between Data Size and Arima Prediction Interval Width?

When we use Arima model to acquire Interval Predictions, will the width of prediction intervals decrease if we use more data (longer history) to fit the model?
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23 views

Is it possible to compare Forex data to similar random time series to measure how predictable it is?

In relation to my previous question (Who influences Forex prices and by how much?) I have an raw idea how to determine how much is Forex influenced externally and how much is its behavior given by its ...
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0answers
57 views

Please help me understand this dataset regarding stock prices

I am supposed to predict column E but I cannot figure out what any of these columns mean. The information provided with the dataset is as follows: column A: past 28 week slope value column B: past 48 ...
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1answer
126 views

Feature engineering for mid-price prediction - quickly changing features

I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
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3answers
252 views

Consistent offset/lag in time-series prediction using Neural Network (all code provided)

I'm using a neural network (keras package) to predict Bitcoin prices 48 hours in advance. The issue is that for some reason, my predictions are "correct" but they are lagging behind the true ...
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1answer
159 views

Reintegrating Fractionally Differentiated Time Series Prediction

I am working on a supervised learning approach to Time Series Regression, and am currently investigating fractionall differentiation (optimizing the stationarity/information tradeoff) discussed ...
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0answers
68 views

Garch models - are they useful for hedging? If so how?

I understand that Garch models are useful to predict volatility. But are they useful for hedging in practice? If I want to hedge volatility, why shouldn't I just use a Variance Swap? In other words, ...
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1answer
145 views

Predict Log Stock Return Direction and Trading Strategy

The $k$ period log return is defined as $$r_{t}(k)=log(S_{t}/S_{t-k}),$$ Where $S_{t}$ is the stock closing price at time $t$. For argument sake, assume that by time I mean a stock trading day and ...
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3answers
122 views

Does asset volume, rather than asset returns, predict performance?

Asset returns are the most common data type used in finance. They are derived from closing price data. Ordinary level 1 data for stocks not only consists of closing prices, but also gross volume ...
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1answer
78 views

Is option surface same as future price probability surface?

Let's consider the Option Chain for the Stock. There are two 3D surfaces representing the probability of the future stock price and the option prices. I wonder if they are representing the same thing? ...
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0answers
23 views

Tradeoffs of using Loess regression to fit random walks

I am curious if anyone has had much experience attempting to predict random walks using Loess regression or a variant of local statistical methods.
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1answer
74 views

How to use multi-periods and mult-factors to predict stock price by linear regression?

Give data in $t_n$ denoted by $[x_1^n, x_2^n, ... x_d^n]$ and label $y_n$ to be predicted. We can just train a $d$-dimensional linear regression $y_n=\sum b_ix_i^n$ to make a prediction. However, I ...
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1answer
38 views

Mutual fund rating predictions

I am working on a dataset with aim to predict the MF ratings. There are cols like, 10 yr, 7 yr, 5 yr etc returns. I also have commencement date of MFs, the question is there are MFs with commencements ...
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1answer
73 views

If a security has many options expiring on a day, can you predict its price direction?

r/wallstreetbets post alleges that because ~$1.9 Trillion USD of SPY call options expire on 3/20, the price of SPY will skyrocket on 3/20. Is this correct? What can be deduced? I'm not expecting to ...
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1answer
81 views

Predicting time series based on another

This is more of a generic question, but I'm sure it has a best answer/methodology which is what I'm trying to reach. I'm trying to figure out a solid line of thought when looking at a time series X ...
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2answers
47 views

Updated Time Series Prediction Model When acquiring new data Points - Basic Question

Suppose I have a Time Series Model (assume ARIMA) and use it to make one-step ahead prediction. If I acquire a new data point, (for example I was originally using the first 100 days to fit an Arima ...
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72 views

Replication of the paper: "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction"

I recently replicated the paper "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction" and found out that my estimation of the equity premium differs from the data provided ...
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1answer
263 views

Question is about the data in the paper: "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction"

I would like to ask a question if you download the data from the Amit Goyal website: http://www.hec.unil.ch/agoyal/ You will see that there are two columns "CRSP_SPvw" "CRSP_SPvwx", they are ...
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15answers
173k views

How can I go about applying machine learning algorithms to stock markets?

I am not very sure, if this question fits in here. I have recently begun, reading and learning about machine learning. Can someone throw some light onto how to go about it or rather can anyone share ...
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0answers
114 views

How to weigh computational cost of updating an online predictive model for latency-constrained trading (e.g., market making, HFT)?

Say one has a predictive online model for market making or HFT (or just for anything strictly latency-constrained). In my specific example, I start with a Gaussian distribution over the "true value" ...
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1answer
156 views

time series data modeling for deep learning

what is the best format to feed the input data, which are time series with varying density over time, to a deep learning network, while at any iteration we want to feed a batch of data including a ...
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4answers
2k views

Can the futures market's open interest predict commodity, treasury, and equity returns?

I came across this article and became curious. Can the futures market's open interest really predict market action?
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0answers
28 views

Which technique determines if var x1 leads var y? Assuming var x1 may need to be transformed

Suppose I want to predict future changes in variable y (stock price over time). I notice that variable x1, inverted and delayed three months, tends to lead y. Which technique can I use to find other ...
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0answers
433 views

Beginner FFT (Fourier) transforms on closing prices for Apple

I don't know math very well, but I have been programming for many years. I would like to use FFT as a parameter to a ML model. The FFT is diving down sharply. I tried many stocks and its the same. ...
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0answers
47 views

Labeling Returns in 5 categories based on BL view approach

I have to label a time series of returns into 5 categories based on the Black Litterman view approach. The categories should look as follows: very bullish: + 2 std. dev. bullish: + 1 std. dev. ...
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0answers
104 views

Mean directional accuracy and zero

I'm trying to use mean directional accuracy to evaluate my directional predictions in back-test, but it can't deal with realised directions which are 0, due to the comparison of the signs of ...
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4answers
322 views

Predicting portfolio returns

I suppose there are roughly two approaches to predict portfolio returns. Either predict the returns of all underlying stocks and aggregate all individual stock predictions, or predict the portfolio ...
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0answers
69 views

Predicting stock returns using principal components of macroeconomic variables

I'm trying to detect return predictability by regressing stock returns on the first couple of principal components of a set of macroeconomic variables. I'm doing this for different stock styles such ...
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1answer
706 views

What machine learning method is more suitable for prediction of financial time series?

I have time series data for various assets and which I transform to create various features. I have framed the problem as a classification task where I attempt to predict either a positive or negative ...
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1answer
3k views

How quants use ML models for stock market prediction

I am learning machine learning to use it for stock market price forecasting. While doing that I got this question. If we take any country with stock exchange they have more than one investment assests ...
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2answers
287 views

Any research on label/target variable design for ML training?

is there any discussion or paper about how to define/design the labels for the ML training? Intuitively I can think of: Net return of the next future day Net return using the max candle-high value of ...
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92 views

Can I make the simple moving average less lagging by this method?

I have (T+3) predicted prices for a stock. Let us assume that the predicted prices are going to be very close to the actual prices. can I alter the formula of simple moving average for 20 days like ...