關於 investment meaning ，我們在網路上蒐集到這些相關的討論、資訊與評價

#NFTsummer A few weeks ago, we interviewed Xixi Huang (AW#17) in Mandarin and as the AppWorks Fellow who helps us out on the blockchain side, Xixi has a lot of ideas that are worth sharing to a bigger audience. Here's his interview in English:

西西 (Xixi) 黃士晉 is an AppWorks #17 alumni and co-founder of Rydeit. His mission is to bring the applications of blockchain to everyday life because he believes that blockchain can make the world a more fair and better place. As an AppWorks Fellow, he is responsible for guiding founders to think about blockchain and providing insights on blockchain-related investment deals. Before AppWorks, he led his previous team to build more than 10 dapps, including famous games in Taiwan such as 柚子打魚 and Shrimp.Finance. Within a month of launching these games, they had amassed over NT$ 100M (~US$ 3.5M) in transactions. His favorite pastime is traveling with his grandma, and his ultimate goal is to persuade her to buy Bitcoin!

Check out what Xixi has to say about blockchain and NFTs.

#NFTevolution

I started to learn how to write smart contracts in 2016, and learned the entire logic of ERC-20 for tokens and ERC-721 for NFTs. While I was learning about NFTs, I realized that the combination of NFTs with video games was very fitting. In video games, the treasure and equipment you collect becomes virtual assets as NFTs. And the process of using smart contracts to exchange items was very efficient on the blockchain.

At the end of 2018, I designed and created a zombie video game, integrating the concept of playing and drawing cards and packs. Within a pack were 3 NFTs with zombies on them. As a player, the more cards with zombies you collect, the higher your score, and the more tokens you can ultimately earn. From this game, I got connected to other game manufacturers and publishers who were also interested in creating virtual game assets through NFTs. We all had the same idea -- NFTs can bring more meaning and value to the players.

However at the time, many people in the market still believed that tokens and games were riddled with fraud. Since the lifecycle of video games can be short, many traditional gaming companies in the end didn’t find it worthwhile to learn how to integrate NFTs or blockchain into their games.

While the nature of NFTs hasn’t changed, the market has evolved. People’s curiosities are growing, more applications are being developed, and more creators are cropping up. I think now is the right time to start talking to companies about how to integrate NFTs or blockchain into their businesses or games.

#NFTplayground

I think there are many opportunities to start a business these days, and people who want to start one should think about coming to blockchain. Not only is there a huge opportunity but the possibilities are endless. And within blockchain, NFTs currently offer the biggest opportunity. However, it can also pose a new challenge to the team’s endurance, because while easy to obtain traction and users with NFTs, the challenge lies in retaining them.

Similar to other blockchain applications in the past few years, when a new concept is invented, it’ll be flushed with many early users. However, because the surrounding infrastructure wasn’t in place, it was difficult for founders to make valuable extensions or applications. And many of these users don’t actually care about these blockchain products and they are only trying to leverage the ecosystem to make a quick buck. Once they earn all that they can, they will move on to another project.

Right now working on NFTs, you can collect a lot of data in a short amount of time and also make some income to keep you afloat. Founders should seize this opportunity to understand these users, then modify and optimize both their products and mentality to retain them.

Since there’s no geographical restriction on blockchain, I would encourage founders to look beyond your country and region in expanding your NFT products. Also, since blockchain is closely related to finance, if you don’t have a good pricing strategy (or token economics) for your NFT products, the prices might fluctuate greatly. You need to be prepared and remind your users of the risks, otherwise you’ll ultimately get scolded!!

#NFTera

This year, I finally consider myself an official NFT collector. I pay special attention to NFTs with contemporary significance, such as Hashmasks. It signals to the public that NFTs are not just for collecting but that game mechanics can also be applied to interact with users. If we look back ten years from now I’m confident to say that the project that really brought the NFT wave in 2021 would be Hashmasks.

In addition to veteran projects like Hashmasks, other collections I care a lot about are related to Metaverse, such as The Sandbox. I believe that the world will become more and more decentralized and virtual. It is very likely that the next generation will be immersed in a virtual world like Ready Player One, so I am also very optimistic about NFTs in digital worlds like The Sandbox.

The possibilities are endless when it comes to NFTs. Right now, few NFTs are connected with our daily lives. For example, I bought a Hashmask. If I want to show it in my apartment, I might still need to print it out. In the future, how will we connect NFTs to the physical world? I believe they can be applied in many ways, including frames, wallets, cars, house deeds, leases, etc. These products in daily life can be combined with NFTs. We just haven’t opened our imagination yet. It’s hard to imagine that only 20 years ago we were having a hard time believing in the power of the internet.

The development of science and technology not only relies on infrastructure, but also the user's learning curve. The world needs to talk about blockchain more, so that everyone will gradually become familiar with it and it’ll continue to evolve.

If you're a founder working in blockchain or NFTs, welcome to apply to AppWorks >> https://bit.ly/3w0WyIl

📜 [專欄新文章] Uniswap v3 Features Explained in Depth

✍️ 田少谷 Shao

📥 歡迎投稿： https://medium.com/taipei-ethereum-meetup #徵技術分享文 #使用心得 #教學文 #medium

Once again the game-changing DEX 🦄 👑

Image source: https://uniswap.org/blog/uniswap-v3/

Outline

0. Intro1. Uniswap & AMM recap2. Ticks 3. Concentrated liquidity4. Range orders: reversible limit orders5. Impacts of v36. Conclusion

0. Intro

The announcement of Uniswap v3 is no doubt one of the most exciting news in the DeFi place recently 🔥🔥🔥

While most have talked about the impact v3 can potentially bring on the market, seldom explain the delicate implementation techniques to realize all those amazing features, such as concentrated liquidity, limit-order-like range orders, etc.

Since I’ve covered Uniswap v1 & v2 (if you happen to know Mandarin, here are v1 & v2), there’s no reason for me to not cover v3 as well ✅

Thus, this article aims to guide readers through Uniswap v3, based on their official whitepaper and examples made on the announcement page. However, one needs not to be an engineer, as not many codes are involved, nor a math major, as the math involved is definitely taught in your high school, to fully understand the following content 😊😊😊

If you really make it through but still don’t get shxt, feedbacks are welcomed! 🙏

There should be another article focusing on the codebase, so stay tuned and let’s get started with some background noise!

1. Uniswap & AMM recap

Before diving in, we have to first recap the uniqueness of Uniswap and compare it to traditional order book exchanges.

Uniswap v1 & v2 are a kind of AMMs (automated market marker) that follow the constant product equation x * y = k, with x & y stand for the amount of two tokens X and Y in a pool and k as a constant.

Comparing to order book exchanges, AMMs, such as the previous versions of Uniswap, offer quite a distinct user experience:

AMMs have pricing functions that offer the price for the two tokens, which make their users always price takers, while users of order book exchanges can be both makers or takers.

Uniswap as well as most AMMs have infinite liquidity¹, while order book exchanges don’t. The liquidity of Uniswap v1 & v2 is provided throughout the price range [0,∞]².

Uniswap as well as most AMMs have price slippage³ and it’s due to the pricing function, while there isn’t always price slippage on order book exchanges as long as an order is fulfilled within one tick.

In an order book, each price (whether in green or red) is a tick. Image source: https://ftx.com/trade/BTC-PERP

¹ though the price gets worse over time; AMM of constant sum such as mStable does not have infinite liquidity

² the range is in fact [-∞,∞], while a price in most cases won’t be negative

³ AMM of constant sum does not have price slippage

2. Tick

The whole innovation of Uniswap v3 starts from ticks.

For those unfamiliar with what is a tick:

Source: https://www.investopedia.com/terms/t/tick.asp

By slicing the price range [0,∞] into numerous granular ticks, trading on v3 is highly similar to trading on order book exchanges, with only three differences:

The price range of each tick is predefined by the system instead of being proposed by users.

Trades that happen within a tick still follows the pricing function of the AMM, while the equation has to be updated once the price crosses the tick.

Orders can be executed with any price within the price range, instead of being fulfilled at the same one price on order book exchanges.

With the tick design, Uniswap v3 possesses most of the merits of both AMM and an order book exchange! 💯💯💯

So, how is the price range of a tick decided?

This question is actually somewhat related to the tick explanation above: the minimum tick size for stocks trading above 1$ is one cent.

The underlying meaning of a tick size traditionally being one cent is that one cent (1% of 1$) is the basis point of price changes between ticks, ex: 1.02 — 1.01 = 0.1.

Uniswap v3 employs a similar idea: compared to the previous/next price, the price change should always be 0.01% = 1 basis point.

However, notice the difference is that in the traditional basis point, the price change is defined with subtraction, while here in Uniswap it’s division.

This is how price ranges of ticks are decided⁴:

Image source: https://uniswap.org/whitepaper-v3.pdf

With the above equation, the tick/price range can be recorded in the index form [i, i+1], instead of some crazy numbers such as 1.0001¹⁰⁰ = 1.0100496621.

As each price is the multiplication of 1.0001 of the previous price, the price change is always 1.0001 — 1 = 0.0001 = 0.01%.

For example, when i=1, p(1) = 1.0001; when i=2, p(2) = 1.00020001.

p(2) / p(1) = 1.00020001 / 1.0001 = 1.0001

See the connection between the traditional basis point 1 cent (=1% of 1$) and Uniswap v3’s basis point 0.01%?

Image source: https://tenor.com/view/coin-master-cool-gif-19748052

But sir, are prices really granular enough? There are many shitcoins with prices less than 0.000001$. Will such prices be covered as well?

Price range: max & min

To know if an extremely small price is covered or not, we have to figure out the max & min price range of v3 by looking into the spec: there is a int24 tick state variable in UniswapV3Pool.sol.

Image source: https://uniswap.org/whitepaper-v3.pdf

The reason for a signed integer int instead of an uint is that negative power represents prices less than 1 but greater than 0.

24 bits can cover the range between 1.0001 ^ (2²³ — 1) and 1.0001 ^ -(2)²³. Even Google cannot calculate such numbers, so allow me to offer smaller values to have a rough idea of the whole price range:

1.0001 ^ (2¹⁸) = 242,214,459,604.341

1.0001 ^ -(2¹⁷) = 0.000002031888943

I think it’s safe to say that with a int24 the range can cover > 99.99% of the prices of all assets in the universe 👌

⁴ For implementation concern, however, a square root is added to both sides of the equation.

How about finding out which tick does a price belong to?

Tick index from price

The answer to this question is rather easy, as we know that p(i) = 1.0001^i, simply takes a log with base 1.0001 on both sides of the equation⁴:

Image source: https://www.codecogs.com/latex/eqneditor.php

Let’s try this out, say we wanna find out the tick index of 1000000.

Image source: https://ncalculators.com/number-conversion/log-logarithm-calculator.htm

Now, 1.0001¹³⁸¹⁶² = 999,998.678087146. Voila!

⁵ This formula is also slightly modified to fit the real implementation usage.

3. Concentrated liquidity

Now that we know how ticks and price ranges are decided, let’s talk about how orders are executed in a tick, what is concentrated liquidity and how it enables v3 to compete with stablecoin-specialized DEXs (decentralized exchange), such as Curve, by improving the capital efficiency.

Concentrated liquidity means LPs (liquidity providers) can provide liquidity to any price range/tick at their wish, which causes the liquidity to be imbalanced in ticks.

As each tick has a different liquidity depth, the corresponding pricing function x * y = k also won’t be the same!

Each tick has its own liquidity depth. Image source: https://uniswap.org/blog/uniswap-v3/

Mmm… examples are always helpful for abstract descriptions 😂

Say the original pricing function is 100(x) * 1000(y) = 100000(k), with the price of X token 1000 / 100 = 10 and we’re now in the price range [9.08, 11.08].

If the liquidity of the price range [11.08, 13.08] is the same as [9.08, 11.08], we don’t have to modify the pricing function if the price goes from 10 to 11.08, which is the boundary between two ticks.

The price of X is 1052.63 / 95 = 11.08 when the equation is 1052.63 * 95 = 100000.

However, if the liquidity of the price range [11.08, 13.08] is two times that of the current range [9.08, 11.08], balances of x and y should be doubled, which makes the equation become 2105.26 * 220 = 400000, which is (1052.63 * 2) * (110 * 2) = (100000 * 2 * 2).

We can observe the following two points from the above example:

Trades always follow the pricing function x * y = k, while once the price crosses the current price range/tick, the liquidity/equation has to be updated.

√(x * y) = √k = L is how we represent the liquidity, as I say the liquidity of x * y = 400000 is two times the liquidity of x * y = 100000, as √(400000 / 100000) = 2.

What’s more, compared to liquidity on v1 & v2 is always spread across [0,∞], liquidity on v3 can be concentrated within certain price ranges and thus results in higher capital efficiency from traders’ swapping fees!

Let’s say if I provide liquidity in the range [1200, 2800], the capital efficiency will then be 4.24x higher than v2 with the range [0,∞] 😮😮😮 There’s a capital efficiency comparison calculator, make sure to try it out!

Image source: https://uniswap.org/blog/uniswap-v3/

It’s worth noticing that the concept of concentrated liquidity was proposed and already implemented by Kyper, prior to Uniswap, which is called Automated Price Reserve in their case.⁵

⁶ Thanks to Yenwen Feng for the information.

4. Range orders: reversible limit orders

As explained in the above section, LPs of v3 can provide liquidity to any price range/tick at their wish. Depending on the current price and the targeted price range, there are three scenarios:

current price < the targeted price range

current price > the targeted price range

current price belongs to the targeted price range

The first two scenarios are called range orders. They have unique characteristics and are essentially fee-earning reversible limit orders, which will be explained later.

The last case is the exact same liquidity providing mechanism as the previous versions: LPs provide liquidity in both tokens of the same value (= amount * price).

There’s also an identical product to the case: grid trading, a very powerful investment tool for a time of consolidation. Dunno what’s grid trading? Check out Binance’s explanation on this, as this topic won’t be covered!

In fact, LPs of Uniswap v1 & v2 are grid trading with a range of [0,∞] and the entry price as the baseline.

Range orders

To understand range orders, we’d have to first revisit how price is discovered on Uniswap with the equation x * y = k, for x & y stand for the amount of two tokens X and Y and k as a constant.

The price of X compared to Y is y / x, which means how many Y one can get for 1 unit of X, and vice versa the price of Y compared to X is x / y.

For the price of X to go up, y has to increase and x decrease.

With this pricing mechanism in mind, it’s example time!

Say an LP plans to place liquidity in the price range [15.625, 17.313], higher than the current price of X 10, when 100(x) * 1000(y) = 100000(k).

The price of X is 1250 / 80 = 15.625 when the equation is 80 * 1250 = 100000.

The price of X is 1315.789 / 76 = 17.313 when the equation is 76 * 1315.789 = 100000.

If now the price of X reaches 15.625, the only way for the price of X to go even higher is to further increase y and decrease x, which means exchanging a certain amount of X for Y.

Thus, to provide liquidity in the range [15.625, 17.313], an LP needs only to prepare 80 — 76 = 4 of X. If the price exceeds 17.313, all 4 X of the LP is swapped into 1315.789 — 1250 = 65.798 Y, and then the LP has nothing more to do with the pool, as his/her liquidity is drained.

What if the price stays in the range? It’s exactly what LPs would love to see, as they can earn swapping fees for all transactions in the range! Also, the balance of X will swing between [76, 80] and the balance of Y between [1250, 1315.789].

This might not be obvious, but the example above shows an interesting insight: if the liquidity of one token is provided, only when the token becomes more valuable will it be exchanged for the less valuable one.

…wut? 🤔

Remember that if 4 X is provided within [15.625, 17.313], only when the price of X goes up from 15.625 to 17.313 is 4 X gradually swapped into Y, the less valuable one!

What if the price of X drops back immediately after reaching 17.313? As X becomes less valuable, others are going to exchange Y for X.

The below image illustrates the scenario of DAI/USDC pair with a price range of [1.001, 1.002] well: the pool is always composed entirely of one token on both sides of the tick, while in the middle 1.001499⁶ is of both tokens.

Image source: https://uniswap.org/blog/uniswap-v3/

Similarly, to provide liquidity in a price range < current price, an LP has to prepare a certain amount of Y for others to exchange Y for X within the range.

To wrap up such an interesting feature, we know that:

Only one token is required for range orders.

Only when the current price is within the range of the range order can LP earn trading fees. This is the main reason why most people believe LPs of v3 have to monitor the price more actively to maximize their income, which also means that LPs of v3 have become arbitrageurs 🤯

I will be discussing more the impacts of v3 in 5. Impacts of v3.

⁷ 1.001499988 = √(1.0001 * 1.0002) is the geometric mean of 1.0001 and 1.0002. The implication is that the geometric mean of two prices is the average execution price within the range of the two prices.

Reversible limit orders

As the example in the last section demonstrates, if there is 4 X in range [15.625, 17.313], the 4 X will be completely converted into 65.798 Y when the price goes over 17.313.

We all know that a price can stay in a wide range such as [10, 11] for quite some time, while it’s unlikely so in a narrow range such as [15.625, 15.626].

Thus, if an LP provides liquidity in [15.625, 15.626], we can expect that once the price of X goes over 15.625 and immediately also 15.626, and does not drop back, all X are then forever converted into Y.

The concept of having a targeted price and the order will be executed after the price is crossed is exactly the concept of limit orders! The only difference is that if the range of a range order is not narrow enough, it’s highly possible that the conversion of tokens will be reverted once the price falls back to the range.

As price ranges follow the equation p(i) = 1.0001 ^ i, the range can be quite narrow and a range order can thus effectively serve as a limit order:

When i = 27490, 1.0001²⁷⁴⁹⁰ = 15.6248.⁸

When i = 27491, 1.0001²⁷⁴⁹¹ = 15.6264.⁸

A range of 0.0016 is not THAT narrow but can certainly satisfy most limit order use cases!

⁸ As mentioned previously in note #4, there is a square root in the equation of the price and index, thus the numbers here are for explantion only.

5. Impacts of v3

Higher capital efficiency, LPs become arbitrageurs… as v3 has made tons of radical changes, I’d like to summarize my personal takes of the impacts of v3:

Higher capital efficiency makes one of the most frequently considered indices in DeFi: TVL, total value locked, becomes less meaningful, as 1$ on Uniswap v3 might have the same effect as 100$ or even 2000$ on v2.

The ease of spot exchanging between spot exchanges used to be a huge advantage of spot markets over derivative markets. As LPs will take up the role of arbitrageurs and arbitraging is more likely to happen on v3 itself other than between DEXs, this gap is narrowed … to what extent? No idea though.

LP strategies and the aggregation of NFT of Uniswap v3 liquidity token are becoming the blue ocean for new DeFi startups: see Visor and Lixir. In fact, this might be the turning point for both DeFi and NFT: the two main reasons of blockchain going mainstream now come to the alignment of interest: solving the $$ problem 😏😏😏

In the right venue, which means a place where transaction fees are low enough, such as Optimism, we might see Algo trading firms coming in to share the market of designing LP strategies on Uniswap v3, as I believe Algo trading is way stronger than on-chain strategies or DAO voting to add liquidity that sort of thing.

After reading this article by Parsec.finance: The Dex to Rule Them All, I cannot help but wonder: maybe there is going to be centralized crypto exchanges adopting v3’s approach. The reason is that since orders of LPs in the same tick are executed pro-rata, the endless front-running speeding-competition issue in the Algo trading world, to some degree, is… solved? 🤔

Anyway, personal opinions can be biased and seriously wrong 🙈 I’m merely throwing out a sprat to catch a whale. Having a different voice? Leave your comment down below!

6. Conclusion

That was kinda tough, isn’t it? Glad you make it through here 🥂🥂🥂

There are actually many more details and also a huge section of Oracle yet to be covered. However, since this article is more about features and targeting normal DeFi users, I’ll leave those to the next one; hope there is one 😅

If you have any doubt or find any mistake, please feel free to reach out to me and I’d try to reply AFAP!

Stay tuned and in the meantime let’s wait and see how Uniswap v3 is again pioneering the innovation of DeFi 🌟

Uniswap v3 Features Explained in Depth was originally published in Taipei Ethereum Meetup on Medium, where people are continuing the conversation by highlighting and responding to this story.

👏 歡迎轉載分享鼓掌

【近日股市資金行情之我見】

這兩個月忙著跨海搬家，沒太多時間寫長文。這篇文章從今年1月斷斷續續寫到現在，主要嘗試回答兩個問題：

1. 2021年初是否存在市場過熱現象？

2. 美國政府2020年的瘋狂印鈔行為（參見下圖）是否會引發嚴重通貨膨脹？投資人應該如何因應？



首先關於第一個問題，在今年2月份我們看到美國股市的option契約數量大增，從19M/每日增加至30M/每日；SPACs形式2020年增加200件，募資$74B；GME軋空炒作行情。

這樣是否存在擦鞋童現象？

這部分我想先回顧17世紀荷蘭鬱金香投機事件。

傳說當年荷蘭鬱金香莖球被瘋狂炒作，價格上漲幾百倍，荷蘭舉國人民紛紛陷入投機熱潮，甚至20世紀德國知名投資客柯斯多蘭尼稱當年有駝背侏儒光是出租其後背供投資客們寫上最金莖球價格，然後穿梭人群中賺了一小筆。

隨後鬱金香莖球炒作泡沫破裂，荷蘭國家經濟受到重創，進而影響當年曾是海上商業王國的地位。

然而史實是如此嗎？

美國經濟學家Peter Garber專門研究此一投機炒作的經濟史，並寫下幾篇著名論文。而依據其著作"Famous First Bubbles The Fundamentals of Early Manias" 一書，我整理幾個重點：

1. 實際上鬱金香熱潮時間相當短，價格明顯彈升發生在1636年11月～1637年1月份。

2. 參與人數總共約350人，全是職業商人；真正支付高價（超過300荷蘭盾）者約莫10人，多數人其實是透過遠期合約的方式進行炒作，而最後多以違約拒絕真實支付現金，直到荷蘭當地鬱金香相關商會與政府出面介入，才以履約價格的10%甚至5%方式解除合約。

3. 非常昂貴的品種，如Semper Augustus 的真實漲幅只有5倍（從原本的1千出頭荷蘭盾漲至5千多），並非都市傳說中幾百倍的漲幅。

漲幅較大的主要是那些本來就平價的品種，例如Gouda buds，起漲價格約２荷蘭盾，最高價50多荷蘭頓。即便存在瘋漲，但至多也是一、二十倍，這即便放在現代農產品供需失調時的價格軌跡來比較，也並不離奇。例如台灣颱風後的香菜價格漲幅。

這邊可以題外話說明為何Semper Augustus這品種售價昂貴。因為這特殊品種本不存在於大自然，而是農夫必須將快開花的鬱金香球莖人工嫁接罹患某種病毒的鬱金香，才能開出特殊花色。而這種嫁接病毒的球莖將會死亡，不再具備繁衍後代的能力。此外，嫁接後的成功率在當年也並不高，不保證存活也不保證開出特殊花色。

物以稀為貴下使得Semper Augustus這品種本來售價就高昂，是一般品種的百倍。

讀者可參見以下幾張當年不同品種的價格走勢圖：

4. 也因為這個事件的範圍與熱潮都比傳說中小得多，因此並未對實質荷蘭資本市場或經濟體造成多少負面影響。

荷蘭鬱金香泡沫事件有三點啟示：

a. 即便在當年差不多時期的著作、媒體都有對其瘋狂投機炒作的描述，但實證來看誇大成分居多。很可能受到作家喜歡站在道德高點批判投機行為的習慣影響，但做為投資人或經濟史研究者在考據曾經的泡沫事件，始終必須以事實為依歸。

b. 小範圍小規模的投機炒作，無論價格哄抬得多麼高聳入天，事實是「毀約」始終是一種選項，有行無市的價格不存在經濟學意義。

c. 同樣地，小範圍小規模的投機炒作，無論價格哄抬得多麼高聳入天，對整體經濟乃至於資本市場的影響同樣不會太大。這意味著我們雖然應該警醒擦鞋童現象，但也無需杯弓蛇影。

如同我在去年幾篇文章中談到的，我認為Covid-19疫情本身造成的經濟損害遠不如人為的隔離措施所造成。目前看到的全世界生產力衰退，人禍成分高過天災。但與2009年不同之處在於：

「Personal savings soared as high as 33.7% in April following the Cares Act and were still a healthy 13.7% in December before Congress passed another $900 billion in Covid aid. This means that, unlike during the 2009 recession, households aren’t weighed down by debt.
Personal bankruptcies, home foreclosures and loan delinquencies last fall were the lowest since at least 2003. The mortgage delinquency rate was 0.7% in the third quarter of 2020 compared to 7% in the first quarter of 2009. ...」

出自Wrong Stimulus, Wrong Time - WSJ （ Feb. 5, 2021）報導。

因此在我看來，此文撰寫的時間點，雖然多多少少某些類股上存在擦鞋童現象，投資人不必過度擔憂。投資人真正該做好未雨綢繆準備的，是美國瘋狂印鈔下必然到來的嚴重通貨膨脹。

問題二：通貨膨脹下股票標的如何選擇？

高資產或高負債的公司在嚴重通膨時期的股價表現優於高現金部位的公司。在經濟學大師Armen A. Alchian 1965年的論文 "Effects of Inflation Upon Stock Prices "中，特別指出傳統經濟學如凱因斯、費雪等著名學者之見認為銀行身為典型債務人，在通貨膨脹環境下應該有較好的股價表現。而Alchian則點出這些學者大老忽略銀行雖然集債務於一身（大眾存款之於銀行就是債務），然而銀行受限於法規與現實，其資產多是「現金資產（money-type assets）」，故在嚴重通貨膨脹影響下，銀行實際經濟損失大過通膨泡沫所得，股價表現當然好不到哪去。

Alchian此文對我的啟發甚大，揭櫫面對貨幣因素影響甚大時的投資方向。

但我們要知道Armen Alchian的論文寫作時期與如今的投資環境又有幾個重大侷限條件之不同，因此我們不能生吞活剝地硬套Alchian的觀點，而是必須真實理解背後隱含的正確經濟學邏輯，並依據當今侷限條件之不同而修改並應用。

引入費雪的利息理論與張五常的財富倉庫概念，現今世界何謂資產、何謂債權債務、何謂現金？我們必須要能超脫會計學、法學的思維侷限，而從真正在投資決策上有效益的經濟學角度切入。

一個我認為值得投資人注意的重點是：投資人對於高商譽（goodwill）的公司能否有正確地、在經濟學層面的深度理解與評價機制。

這點同樣也適用在面對新科技寵兒如電動車之流之正確價格評估。

以長期投資角度看，如果以夠低的成本入手高資產或高品牌價值公司，本身部位又很大，則隨後的股市大幅修正甚至崩盤基本可以無視

如果部位不大，則可以視隨後散戶瘋狂狀況逐漸增加現金部位。

回到現實面，我認為通膨現象確實在發生，有兩個現象值得注意：

a. 機構法人買入加密貨幣的金流增加

「...JPMorgan, said the size of the bitcoin market had grown to equal about a fifth of gold held for investment and trading purposes, with a market capitalisation for the cryptocurrency of $750bn at its peak earlier this year, meaning it “is far from a niche asset class”. 」

「...Analysts at Canadian insurance company Manulife said in late January that the expansion in central banks’ balance sheets and rising public debt would push investors further into alternative asset classes ...」

「...Xangle showing that investors have lost more than $16bn to fraud since 2012 ...」

b. 近日美國美國前25大銀行對私人之貸款佔總資產比例從去年54.1%下降之45.7%，且放在Fed reserve account總金額達$3.15兆美元。

（The 25 largest U.S. banks currently hold 45.7% of their assets in loans and leases, according to Fed data released Friday, down from 54.1% this time last year. .. reserve balances in their Federal Reserve depository accounts at sky-high levels, $3.15 trillion at present
）

通膨現象將會更嚴重，因為「...According to a recent House Budget Committee estimate, $1 trillion from last year’s bills hasn’t been spent—including $59 billion for schools, $239 billion for health care and $452 billion in small business loans. State and local governments added 67,000 jobs in January. They don’t need more federal cash. ...」
WSJ "wrong-stimulus-wrong-time " Feb. 5, 2021

如同我在「論比特幣」一文中闡述過：比特幣顯然不是被當作交易貨幣而是某種無根財富倉庫，因此其價格之暴漲暴跌均同時具備「合理與不合理」之雙面性。因為不存在適當的評價方式去推估其價格之合理性。

但在此文我想進一步指出，從另一層面來看，這種無根倉庫的價格變動本身卻可提供我們對於貨幣因素下真實通貨膨脹的現狀診斷。這好比我們切脈在左關中層把得一數滑脈，搭配右關心位或肝位的脈相，或胃經、肝經或經外奇穴的壓痛診斷，或舌診眼診等等訊息，我們可以推知患者是肝臟、胰臟有惡性腫瘤亦或慢性胃潰瘍。

比特幣的暴漲本身也是一個類似性質的市場訊號。

換言之，當我們把貨幣看做經濟體的血液/體液時，投資人懂不懂得把經濟的脈？是否可以從貨幣的脈相得知經濟血液/體液的品質、健康度、病理變異方向程度與進程...等等。當我們脈診上發現血液/體液堆積於某經絡時，我們看到某類型資產價格飛漲甚至軋空時，診斷者有沒有能力正確推測隨後的、不同時間點地病程發展與相對應的症狀發作？

中美貿易戰框架與因應Covid-19疫情的政府舉措則是結構性地在解剖學層面改變經濟體本身，所需要的制度經濟學知識又是否足夠投資人能趨吉避凶甚或從中獲利？

這些都是參與投資市場者必須時時捫心自問的問題。

我文末再強調一次：美國主要銀行減少對私人企業放款而增加手中政府債券這現象。

參考：

Financial Times "Bitcoin boom backstopped by central banks’ easy-money policies" 2021/2/4

Financial Times "US mortgage executives forecast a $3tn year in 2021 " 2021/01/08

WSJ "For One GameStop Trader, the Wild Ride Was Almost as Good as the Enormous Payoff " 2021/02/03

Armen A. Alchian, "Effects of Inflation Upon Stock Prices" (1965)

Peter M. Garber, Famous First Bubbles The Fundamentals of Early Manias (2000）

WSJ, "Fed Policy Is Smothering Private Lending" (2021/03/08)

文章連結：
https://ppt.cc/f7YCNx