What An Inverted Yield Curve Means For Investors
The yield spread between 10-year and 2-year US Treasury bonds has fallen below 0.2 percent, its lowest level since March 2020. A flattening or negative yield curve can be a bad sign for the economy.
What Is An Inverted Yield Curve?
In the yield curve, bonds of equal credit quality but different maturities are plotted. The most commonly used yield curve for US investors is a plot of 2-year and 10-year Treasury yields, which have yet to invert.
A typical yield curve has higher interest rates for future maturities. In a flat yield curve, short-term and long-term yields are similar. Inverted yield curves occur when short-term yields exceed long-term yields. Inversions of yield curves have historically occurred during recessions.
Inverted yield curves have preceded each of the past eight US recessions. The good news is they're far leading indicators, meaning a recession is likely not imminent.
Every US recession since 1955 has occurred between six and 24 months after an inversion of the two-year and 10-year Treasury yield curves, according to the San Francisco Fed. So, six months before COVID-19, the yield curve inverted in August 2019.
The spread between two-year and 10-year Treasury yields was 0.18 percent on Tuesday, the smallest since before the last US recession. If the graph above continues, a two-year/10-year yield curve inversion could occur within the next few months.
According to Bank of America analyst Stephen Suttmeier, the S&P 500 typically peaks six to seven months after the 2s-10s yield curve inverts, and the US economy enters recession six to seven months later.
Investors appear unconcerned about the flattening yield curve. This is in contrast to the iShares 20+ Year Treasury Bond ETF TLT +2.19% which was down 1% on Tuesday.
Inversion of the yield curve and rising interest rates have historically harmed stocks. Recessions in the US have historically coincided with or followed the end of a Federal Reserve rate hike cycle, not the start.
More on Economics & Investing
1 year ago
Mystery of the $1 billion'meme stock' that went to $400 billion in days
Who is AMTD Digital?
An unknown Hong Kong corporation joined the global megacaps worth over $500 billion on Tuesday.
The American Depository Share (ADS) with the ticker code HKD gapped at the open, soaring 25% over the previous closing price as trading began, before hitting an intraday high of $2,555.
At its peak, its market cap was almost $450 billion, more than Facebook parent Meta or Alibaba.
Yahoo Finance reported a daily volume of 350,500 shares, the lowest since the ADS began trading and much below the average of 1.2 million.
Despite losing a fifth of its value on Wednesday, it's still worth more than Toyota, Nike, McDonald's, or Walt Disney.
The company sold 16 million shares at $7.80 each in mid-July, giving it a $1 billion market valuation.
Why the boom?
That market cap seems unjustified.
According to SEC reports, its income-generating assets barely topped $400 million in March. Fortune's emails and calls went unanswered.
Website discloses little about company model. Its one-minute business presentation film uses a Star Wars–like design to sell the company as a "one-stop digital solutions platform in Asia"
The SEC prospectus explains.
AMTD Digital sells a "SpiderNet Ecosystems Solutions" kind of club membership that connects enterprises. This is the bulk of its $25 million annual revenue in April 2021.
Pretax profits have been higher than top line over the past three years due to fair value accounting gains on Appier, DayDayCook, WeDoctor, and five Asian fintechs.
AMTD Group, the company's parent, specializes in investment banking, hotel services, luxury education, and media and entertainment. AMTD IDEA, a $14 billion subsidiary, is also traded on the NYSE.
Why AMTD Digital listed in the U.S. is unknown, as it informed investors in its share offering prospectus that could delist under SEC guidelines.
Beijing's red tape prevents the Sarbanes-Oxley Board from inspecting its Chinese auditor.
This frustrates Chinese stock investors. If the U.S. and China can't achieve a deal, 261 Chinese companies worth $1.3 trillion might be delisted.
Calvin Choi left UBS to become AMTD Group's CEO.
His capitalist background and status as a Young Global Leader with the World Economic Forum don't stop him from praising China's Communist party or celebrating the "glory and dream of the Great Rejuvenation of the Chinese nation" a century after its creation.
Despite having an executive vice chairman with a record of battling corruption and ties to Carrie Lam, Beijing's previous proconsul in Hong Kong, Choi is apparently being targeted for a two-year industry ban by the city's securities regulator after an investor accused Choi of malfeasance.
Some CMIG-funded initiatives produced money, but he didn't give us the proceeds, a corporate official told China's Caixin in October 2020. We don't know if he misappropriated or lost some money.
A seismic anomaly
In fundamental analysis, where companies are valued based on future cash flows, AMTD Digital's mind-boggling market cap is a statistical aberration that should occur once every hundred years.
AMTD Digital doesn't know why it's so valuable. In a thank-you letter to new shareholders, it said it was confused by the stock's performance.
Since its IPO, the company has seen significant ADS price volatility and active trading volume, it said Tuesday. "To our knowledge, there have been no important circumstances, events, or other matters since the IPO date."
Permabears awoke after the jump. Jim Chanos asked if "we're all going to ignore the $400 billion meme stock in the room," while Nate Anderson called AMTD Group "sketchy."
It happened the same day SEC Chair Gary Gensler praised the 20th anniversary of the Sarbanes-Oxley Act, aimed to restore trust in America's financial markets after the Enron and WorldCom accounting fraud scandals.
The run-up revived unpleasant memories of Robinhood's decision to limit retail investors' ability to buy GameStop, regarded as a measure to protect hedge funds invested in the meme company.
Why wasn't HKD's buy button removed? Because retail wasn't behind it?" tweeted Gensler on Tuesday. "Real stock fraud. "You're worthless."
Sofien Kaabar, CFA
10 months ago
How to Make a Trading Heatmap
Python Heatmap Technical Indicator
Heatmaps provide an instant overview. They can be used with correlations or to predict reactions or confirm the trend in trading. This article covers RSI heatmap creation.
The Market System
Bullish trend: The market tends to make higher highs, which indicates that the overall trend is upward.
Sideways: The market tends to fluctuate while staying within predetermined zones.
Bearish trend: The market has the propensity to make lower lows, indicating that the overall trend is downward.
Most tools detect the trend, but we cannot predict the next state. The best way to solve this problem is to assume the current state will continue and trade any reactions, preferably in the trend.
If the EURUSD is above its moving average and making higher highs, a trend-following strategy would be to wait for dips before buying and assuming the bullish trend will continue.
Indicator of Relative Strength
J. Welles Wilder Jr. introduced the RSI, a popular and versatile technical indicator. Used as a contrarian indicator to exploit extreme reactions. Calculating the default RSI usually involves these steps:
Determine the difference between the closing prices from the prior ones.
Distinguish between the positive and negative net changes.
Create a smoothed moving average for both the absolute values of the positive net changes and the negative net changes.
Take the difference between the smoothed positive and negative changes. The Relative Strength RS will be the name we use to describe this calculation.
To obtain the RSI, use the normalization formula shown below for each time step.
The 13-period RSI and black GBPUSD hourly values are shown above. RSI bounces near 25 and pauses around 75. Python requires a four-column OHLC array for RSI coding.
import numpy as np def add_column(data, times): for i in range(1, times + 1): new = np.zeros((len(data), 1), dtype = float) data = np.append(data, new, axis = 1) return data def delete_column(data, index, times): for i in range(1, times + 1): data = np.delete(data, index, axis = 1) return data def delete_row(data, number): data = data[number:, ] return data def ma(data, lookback, close, position): data = add_column(data, 1) for i in range(len(data)): try: data[i, position] = (data[i - lookback + 1:i + 1, close].mean()) except IndexError: pass data = delete_row(data, lookback) return data def smoothed_ma(data, alpha, lookback, close, position): lookback = (2 * lookback) - 1 alpha = alpha / (lookback + 1.0) beta = 1 - alpha data = ma(data, lookback, close, position) data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta) for i in range(lookback + 2, len(data)): try: data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta) except IndexError: pass return data def rsi(data, lookback, close, position): data = add_column(data, 5) for i in range(len(data)): data[i, position] = data[i, close] - data[i - 1, close] for i in range(len(data)): if data[i, position] > 0: data[i, position + 1] = data[i, position] elif data[i, position] < 0: data[i, position + 2] = abs(data[i, position]) data = smoothed_ma(data, 2, lookback, position + 1, position + 3) data = smoothed_ma(data, 2, lookback, position + 2, position + 4) data[:, position + 5] = data[:, position + 3] / data[:, position + 4] data[:, position + 6] = (100 - (100 / (1 + data[:, position + 5]))) data = delete_column(data, position, 6) data = delete_row(data, lookback) return data
Make sure to focus on the concepts and not the code. You can find the codes of most of my strategies in my books. The most important thing is to comprehend the techniques and strategies.
My weekly market sentiment report uses complex and simple models to understand the current positioning and predict the future direction of several major markets. Check out the report here:
Using the Heatmap to Find the Trend
RSI trend detection is easy but useless. Bullish and bearish regimes are in effect when the RSI is above or below 50, respectively. Tracing a vertical colored line creates the conditions below. How:
When the RSI is higher than 50, a green vertical line is drawn.
When the RSI is lower than 50, a red vertical line is drawn.
Zooming out yields a basic heatmap, as shown below.
def indicator_plot(data, second_panel, window = 250): fig, ax = plt.subplots(2, figsize = (10, 5)) sample = data[-window:, ] for i in range(len(sample)): ax.vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1) if sample[i, 3] > sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5) if sample[i, 3] < sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) if sample[i, 3] == sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) ax.grid() for i in range(len(sample)): if sample[i, second_panel] > 50: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5) if sample[i, second_panel] < 50: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5) ax.grid() indicator_plot(my_data, 4, window = 500)
Call RSI on your OHLC array's fifth column. 4. Adjusting lookback parameters reduces lag and false signals. Other indicators and conditions are possible.
Another suggestion is to develop an RSI Heatmap for Extreme Conditions.
Contrarian indicator RSI. The following rules apply:
Whenever the RSI is approaching the upper values, the color approaches red.
The color tends toward green whenever the RSI is getting close to the lower values.
Zooming out yields a basic heatmap, as shown below.
import matplotlib.pyplot as plt def indicator_plot(data, second_panel, window = 250): fig, ax = plt.subplots(2, figsize = (10, 5)) sample = data[-window:, ] for i in range(len(sample)): ax.vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1) if sample[i, 3] > sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5) if sample[i, 3] < sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) if sample[i, 3] == sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) ax.grid() for i in range(len(sample)): if sample[i, second_panel] > 90: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5) if sample[i, second_panel] > 80 and sample[i, second_panel] < 90: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5) if sample[i, second_panel] > 70 and sample[i, second_panel] < 80: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5) if sample[i, second_panel] > 60 and sample[i, second_panel] < 70: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5) if sample[i, second_panel] > 50 and sample[i, second_panel] < 60: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5) if sample[i, second_panel] > 40 and sample[i, second_panel] < 50: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5) if sample[i, second_panel] > 30 and sample[i, second_panel] < 40: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5) if sample[i, second_panel] > 20 and sample[i, second_panel] < 30: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5) if sample[i, second_panel] > 10 and sample[i, second_panel] < 20: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5) if sample[i, second_panel] > 0 and sample[i, second_panel] < 10: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5) ax.grid() indicator_plot(my_data, 4, window = 500)
Dark green and red areas indicate imminent bullish and bearish reactions, respectively. RSI around 50 is grey.
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation.
Technical analysis will lose its reputation as subjective and unscientific.
When you find a trading strategy or technique, follow these steps:
Put emotions aside and adopt a critical mindset.
Test it in the past under conditions and simulations taken from real life.
Try optimizing it and performing a forward test if you find any potential.
Transaction costs and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be considered in your tests.
After checking the above, monitor the strategy because market dynamics may change and make it unprofitable.
Theresa W. Carey
1 year ago
How Payment for Order Flow (PFOF) Works
What is PFOF?
PFOF is a brokerage firm's compensation for directing orders to different parties for trade execution. The brokerage firm receives fractions of a penny per share for directing the order to a market maker.
Each optionable stock could have thousands of contracts, so market makers dominate options trades. Order flow payments average less than $0.50 per option contract.
Order Flow Payments (PFOF) Explained
The proliferation of exchanges and electronic communication networks has complicated equity and options trading (ECNs) Ironically, Bernard Madoff, the Ponzi schemer, pioneered pay-for-order-flow.
In a December 2000 study on PFOF, the SEC said, "Payment for order flow is a method of transferring trading profits from market making to brokers who route customer orders to specialists for execution."
Given the complexity of trading thousands of stocks on multiple exchanges, market making has grown. Market makers are large firms that specialize in a set of stocks and options, maintaining an inventory of shares and contracts for buyers and sellers. Market makers are paid the bid-ask spread. Spreads have narrowed since 2001, when exchanges switched to decimals. A market maker's ability to play both sides of trades is key to profitability.
A broker receives fees from a third party for order flow, sometimes without a client's knowledge. This invites conflicts of interest and criticism. Regulation NMS from 2005 requires brokers to disclose their policies and financial relationships with market makers.
Your broker must tell you if it's paid to send your orders to specific parties. This must be done at account opening and annually. The firm must disclose whether it participates in payment-for-order-flow and, upon request, every paid order. Brokerage clients can request payment data on specific transactions, but the response takes weeks.
Order flow payments save money. Smaller brokerage firms can benefit from routing orders through market makers and getting paid. This allows brokerage firms to send their orders to another firm to be executed with other orders, reducing costs. The market maker or exchange benefits from additional share volume, so it pays brokerage firms to direct traffic.
Retail investors, who lack bargaining power, may benefit from order-filling competition. Arrangements to steer the business in one direction invite wrongdoing, which can erode investor confidence in financial markets and their players.
It has always been controversial. Several firms offering zero-commission trades in the late 1990s routed orders to untrustworthy market makers. During the end of fractional pricing, the smallest stock spread was $0.125. Options spreads widened. Traders found that some of their "free" trades cost them a lot because they weren't getting the best price.
The SEC then studied the issue, focusing on options trades, and nearly decided to ban PFOF. The proliferation of options exchanges narrowed spreads because there was more competition for executing orders. Options market makers said their services provided liquidity. In its conclusion, the report said, "While increased multiple-listing produced immediate economic benefits to investors in the form of narrower quotes and effective spreads, these improvements have been muted with the spread of payment for order flow and internalization."
The SEC allowed payment for order flow to continue to prevent exchanges from gaining monopoly power. What would happen to trades if the practice was outlawed was also unclear. SEC requires brokers to disclose financial arrangements with market makers. Since then, the SEC has watched closely.
2020 Order Flow Payment
Rule 605 and Rule 606 show execution quality and order flow payment statistics on a broker's website. Despite being required by the SEC, these reports can be hard to find. The SEC mandated these reports in 2005, but the format and reporting requirements have changed over the years, most recently in 2018.
Brokers and market makers formed a working group with the Financial Information Forum (FIF) to standardize order execution quality reporting. Only one retail brokerage (Fidelity) and one market maker remain (Two Sigma Securities). FIF notes that the 605/606 reports "do not provide the level of information that allows a retail investor to gauge how well a broker-dealer fills a retail order compared to the NBBO (national best bid or offer’) at the time the order was received by the executing broker-dealer."
In the first quarter of 2020, Rule 606 reporting changed to require brokers to report net payments from market makers for S&P 500 and non-S&P 500 equity trades and options trades. Brokers must disclose payment rates per 100 shares by order type (market orders, marketable limit orders, non-marketable limit orders, and other orders).
Richard Repetto, Managing Director of New York-based Piper Sandler & Co., publishes a report on Rule 606 broker reports. Repetto focused on Charles Schwab, TD Ameritrade, E-TRADE, and Robinhood in Q2 2020. Repetto reported that payment for order flow was higher in the second quarter than the first due to increased trading activity, and that options paid more than equities.
Repetto says PFOF contributions rose overall. Schwab has the lowest options rates, while TD Ameritrade and Robinhood have the highest. Robinhood had the highest equity rating. Repetto assumes Robinhood's ability to charge higher PFOF reflects their order flow profitability and that they receive a fixed rate per spread (vs. a fixed rate per share by the other brokers).
Robinhood's PFOF in equities and options grew the most quarter-over-quarter of the four brokers Piper Sandler analyzed, as did their implied volumes. All four brokers saw higher PFOF rates.
TD Ameritrade took the biggest income hit when cutting trading commissions in fall 2019, and this report shows they're trying to make up the shortfall by routing orders for additional PFOF. Robinhood refuses to disclose trading statistics using the same metrics as the rest of the industry, offering only a vague explanation on their website.
Payment for order flow has become a major source of revenue as brokers offer no-commission equity (stock and ETF) orders. For retail investors, payment for order flow poses a problem because the brokerage may route orders to a market maker for its own benefit, not the investor's.
Infrequent or small-volume traders may not notice their broker's PFOF practices. Frequent traders and those who trade larger quantities should learn about their broker's order routing system to ensure they're not losing out on price improvement due to a broker prioritizing payment for order flow.
This post is a summary. Read full article here
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Jano le Roux
11 months ago
My Top 11 Tools For Building A Modern Startup, With A Free Plan
The best free tools are probably unknown to you.
Modern startups are easy to build.
Start with free tools.
Web development — Webflow
Code-free HTML, CSS, and JS.
Webflow isn't like Squarespace, Wix, or Shopify.
It's a super-fast no-code tool for professionals to construct complex, highly-responsive websites and landing pages.
Webflow can help you add animations like those on Apple's website to your own site.
I made the jump from WordPress a few years ago and it changed my life.
No damn plugins. No damn errors. No damn updates.
The best, you can get started on Webflow for free.
Data tracking — Airtable
Airtable combines spreadsheet flexibility with database power without code.
Airtable is modern.
Airtable has modularity.
Scaling Airtable is simple.
Airtable, one of the most adaptable solutions on this list, is perfect for client data management.
Clients choose customized service packages. Airtable consolidates data so you can automate procedures like invoice management and focus on your strengths.
Airtable connects with so many tools that rarely creates headaches. Airtable scales when you do.
Airtable's flexibility makes it a potential backend database.
Design — Figma
Better, faster, easier user interface design.
First, design in Figma.
Export development assets.
Figma lets you add more team members as your company grows to work on each iteration simultaneously.
Figma is web-based, so you don't need a powerful PC or Mac to start.
Task management — Trello
Tacky and terrifying task management products abound. Trello isn’t.
Those that follow Marie Kondo will appreciate Trello.
Everything is clean.
Nothing is complicated.
Everything has a place.
Compared to other task management solutions, Trello is limited. And that’s good. Too many buttons lead to too many decisions lead to too many hours wasted.
Trello is a must for teamwork.
Domain email — Zoho
Free domain email hosting.
Professional email is essential for startups. People relied on monthly payments for too long. Nope.
Zoho offers 5 free professional emails.
It doesn't have Google's UI, but it works.
VPN — Proton VPN
Fast Swiss VPN protects your data and privacy.
Proton VPN is secure.
Proton doesn't record any data.
Proton is based in Switzerland.
Swiss privacy regulation is among the most strict in the world, therefore user data are protected. Switzerland isn't a 14 eye country.
Journalists and activists trust Proton to secure their identities while accessing and sharing information authoritarian governments don't want them to access.
Web host — Netlify
Free fast web hosting.
Netlify is a scalable platform that combines your favorite tools and APIs to develop high-performance sites, stores, and apps through GitHub.
Serverless functions and environment variables preserve API keys.
Netlify's free tier is unmissable.
100GB of free monthly bandwidth.
Free 125k serverless operations per website each month.
Database — MongoDB
Create a fast, scalable database.
MongoDB is for small and large databases. It's a fast and inexpensive database.
Free for the first million reads.
Then, for each million reads, you must pay $0.10.
MongoDB's free plan has:
Encryption from end to end
field-level client-side encryption
If you have a large database, you can easily connect MongoDB to Webflow to bypass CMS limits.
Automation — Zapier
Time-saving tip: automate repetitive chores.
Zapier simplifies life.
Zapier syncs and connects your favorite apps to do impossibly awesome things.
If your online store is connected to Zapier, a customer's purchase can trigger a number of automated actions, such as:
The customer is being added to an email chain.
Put the information in your Airtable.
Send a pre-programmed postcard to the customer.
Alexa, set the color of your smart lights to purple.
Zapier scales when you do.
Email & SMS marketing — Omnisend
Email and SMS marketing campaigns.
This is an excellent Mailchimp option for magical emails. Omnisend's processes simplify email automation.
I love the interface's cleanliness.
Omnisend's free tier includes web push notifications.
Send up to:
500 emails per month
60 maximum SMSs
500 Web Push Maximum
Forms and surveys — Tally
Create flexible forms that people enjoy.
Typeform is clean but restricting. Sometimes you need to add many questions. Tally's needed sometimes.
Tally is flexible and cheaper than Typeform.
99% of Tally's features are free and unrestricted, including:
Tally lets you examine what individuals contributed to forms before submitting them to see where they get stuck.
Airtable and Zapier connectors automate things further. If you pay, you can apply custom CSS to fit your brand.
Free tools are the greatest.
Let's use them to launch a startup.
1 year ago
Payouts to founders at IPO
How much do startup founders make after an IPO? We looked at 2018's major tech IPOs. Paydays aren't what founders took home at the IPO (shares are normally locked up for 6 months), but what they were worth at the IPO price on the day the firm went public. It's not cash, but it's nice. Here's the data.
Several points are noteworthy.
Huge payoffs. Median and average pay were $399m and $918m. Average and median homeownership were 9% and 12%.
Coinbase, Uber, UI Path. Uber, Zoom, Spotify, UI Path, and Coinbase founders raised billions. Zoom's founder owned 19% and Spotify's 28% and 13%. Brian Armstrong controlled 20% of Coinbase at IPO and was worth $15bn. Preserving as much equity as possible by staying cash-efficient or raising at high valuations also helps.
The smallest was Ping. Ping's compensation was the smallest. Andre Duand owned 2% but was worth $20m at IPO. That's less than some billion-dollar paydays, but still good.
IPOs can be lucrative, as you can see. Preserving equity could be the difference between a $20mm and $15bln payday (Coinbase).
1 year ago
Gran Turismo 7 Update Eases Up On The Grind After Fan Outrage
Polyphony Digital has changed the game after apologizing in March.
To make amends for some disastrous downtime, Gran Turismo 7 director Kazunori Yamauchi announced a credits handout and promised to “dramatically change GT7's car economy to help make amends” last month. The first of these has arrived.
The game's 1.11 update includes the following concessions to players frustrated by the economy and its subsequent grind:
The last half of the World Circuits events have increased in-game credit rewards.
Modified Arcade and Custom Race rewards
Clearing all circuit layouts with Gold or Bronze now rewards In-game Credits. Exiting the Sector selection screen with the Exit button will award Credits if an event has already been cleared.
Increased Credits Rewards in Lobby and Daily Races
Increased the free in-game Credits cap from 20,000,000 to 100,000,000.
Additionally, “The Human Comedy” missions are one-hour endurance races that award “up to 1,200,000” credits per event.
This isn't everything Yamauchi promised last month; he said it would take several patches and updates to fully implement the changes. Here's a list of everything he said would happen, some of which have already happened (like the World Cup rewards and credit cap):
- Increase rewards in the latter half of the World Circuits by roughly 100%.
- Added high rewards for all Gold/Bronze results clearing the Circuit Experience.
- Online Races rewards increase.
- Add 8 new 1-hour Endurance Race events to Missions. So expect higher rewards.
- Increase the non-paid credit limit in player wallets from 20M to 100M.
- Expand the number of Used and Legend cars available at any time.
- With time, we will increase the payout value of limited time rewards.
- New World Circuit events.
- Missions now include 24-hour endurance races.
- Online Time Trials added, with rewards based on the player's time difference from the leader.
- Make cars sellable.
The full list of updates and changes can be found here.
Read the original post.